Capacity & latency01 / 08
p99 and time to first token hold up when active users multiply.
Engineering delivery · Applied AI
Digital Gateway is an engineering delivery partner for high-throughput and applied AI solutions.
From solution design and development to integration, launch, support, and end-user training.
You built the product. We scale it to enterprise-grade, under real load and inside regulated environments (HIPAA, SOC 2).
Scale solution to enterprise-grade
Engineering teams to scale applied AI for enterprise demand.
We assemble the engineering team your scale target calls for — with the right skills, team size, timing, and technology stack.
Provider capacity02 / 08
Request throughput stays controlled across quota windows, retries and provider failures.
Data & retrieval03 / 08
Retrieval remains fast, fresh and relevant as the corpus and tenant filters grow.
Enterprise expansion04 / 08
New tenants and regions launch through a repeatable, isolated delivery path.
Release confidence05 / 08
Model, prompt, retrieval and data changes keep critical user tasks working.
Operating economics06 / 08
Cost per successful task remains visible and controlled as usage grows.
Security & compliance07 / 08
The deployment fits the buyer’s data boundary and produces evidence for security review.
Cloud platform & operations08 / 08
Cloud capacity, deployments and recovery keep pace with enterprise rollout.
Peak concurrency
p99 and time to first token hold up when active users multiply.
Solution requirements
- Size from real work.Define peak concurrent requests and arrival bursts, then split traffic by chat, RAG, voice, agents and batch jobs. Include input and output token lengths, session length and tool-call fan-out; active-user counts alone are not a load model.
- Budget the whole user journey.Give the gateway, retrieval and tools, request queue, model prefill and decode, and streaming path separate time budgets. Set targets for first usable output as well as the completed response.
- Measure from the client edge.Record client-observed time to first token and end-to-end latency. Also trace queue and model time separately: a model-server TTFT may start after scheduling and hide the wait users actually feel.
- Keep interactive work out of an unbounded queue.Use admission control, per-journey and per-tenant concurrency limits, request deadlines and cancellation. Decide up front which work waits, goes async or is rejected when capacity runs out.
- Hold warm headroom for bursts.Size the baseline for the expected spike and a lost replica. Scale on queue depth, active requests and latency, with measured replica start time; CPU alone will not tell you when inference is saturated.
- Protect short turns from long prompts.Test a short chat or voice turn arriving during a long-context prefill. If it gets stuck behind that work, separate traffic classes or change scheduling before increasing the concurrency limit.
- Tune for the tail, not peak tokens per second.Compare batching, concurrency caps and token limits against p99 TTFT and inter-token gaps. For self-hosted inference, watch KV-cache pressure and preemption; for hosted models, separate provider limits from app capacity.
- Check the rest of the path.Load retrieval, tool calls, checkpoints, connection pools and the streaming gateway together with inference. A fast model does not rescue a serialized database write or an exhausted connection pool.
- Bound fan-out and retries.Cap model and tool calls per user turn. Retry only where safe, with jitter and a deadline, and stop work when the caller disconnects; otherwise a burst becomes its own retry storm.
- Treat streaming as part of capacity.Size open SSE or WebSocket connections and test disconnects, timeouts and stalled streams. Measure gaps between output chunks, not just the first token.
- Make overload deliberate.Use bounded queues and backpressure. When the agreed peak is exceeded, return a clear retry or async path instead of leaving users with hanging chats and silent timeouts.
- Make the slowest requests explainable.Correlate p99 with journey, tenant, region, prompt length, queue time, retrieval and tool time, cache misses, retries and scaling events. One aggregate latency chart is not enough to find the bottleneck.
- Keep a regression gate.Rerun the same load profile after changes to the model, prompt length, runtime, orchestration or deployment. Canary the change and roll back if the agreed latency or error budget moves the wrong way.
Acceptance criteria
- A test contract exists before tuning.The team signs off on peak concurrency and arrival pattern, workload mix, token-length distributions, test duration, minimum sample count, and numeric p95/p99 TTFT, end-to-end latency, streaming-gap and error targets for each critical journey.
- The test runs through the real path.Traffic enters through the same auth, gateway, retrieval or tools, model and streaming path users will take. The load generator proves it delivered the planned arrival rate; client-side waiting, timeouts and failed requests remain in the results.
- Steady peak passes without hidden backlog.At the contracted peak for the agreed duration, latency and success-rate targets hold, completed throughput matches incoming work, and the pending queue does not keep growing.
- The burst has a bounded recovery.On a realistic ramp and sudden spike to the agreed peak, p99 and TTFT stay within target or the documented overload path activates. The queue drains within the agreed recovery window.
- Mixed workloads do not starve short turns.Run short and long prompts, fresh and long sessions, plus RAG or agent journeys in production proportions. A long prefill must not push the short interactive journey beyond its own p99 target.
- Scale-out and failure are measured at peak.Start a cold replica and remove one serving instance while the test is running. Record time to healthy capacity, user-visible latency, failed streams and recovery; do not infer this from a warm benchmark.
- One heavy tenant cannot take the others down.Drive one tenant to its limit while normal traffic continues elsewhere. Other tenants still meet their latency and error targets, and the configured tenant limits actually engage.
- A sustained run stays stable.During the agreed soak period, p99, queue depth, memory or KV-cache use, open connections and retry volume do not trend upward toward failure.
- The capacity cliff is known.Sweep beyond the contracted peak to show where throughput flattens and p99 bends upward. Above that point, requests queue, shed or degrade as designed instead of timing out across the system.
- Evidence is reproducible and becomes a release gate.Keep the traffic profile, anonymized test inputs, exact model and deployment configuration, scripts, raw latency breakdowns and failure traces. Rerun after fixes and compare future releases with the same baseline.
Throughput & 429s
Request throughput stays controlled across quota windows, retries and provider failures.
Solution requirements
- Map the real limits.Record RPM, TPM, concurrency and burst windows for each model, deployment, region and account. A published minute quota can still throttle a short spike.
- Count tokens before admission.Reserve capacity for the prompt, likely output and agent fan-out; reconcile estimates with actual usage after completion.
- Share one rate-limit view.Coordinate limits across all workers and replicas. A limiter local to each process overbooks the same provider quota.
- Separate traffic classes.Give interactive requests a bounded lane; schedule batch work against spare capacity instead of letting imports block live users.
- Use deadlines, not an infinite queue.Admit, delay, move async or reject each job according to its user-facing deadline and the queue wait already incurred.
- Read the provider signal.Handle Retry-After or reset hints where available, with jitter. Distinguish throttling, provider overload, expired credentials and exhausted spend.
- Budget retries across the whole chain.Cap attempts and spend across nested model and tool calls, not only one HTTP call. Stop retries after the caller cancels or the deadline expires.
- Make side effects retry-safe.Give jobs and downstream actions stable IDs; persist a completed result before acknowledging work. Mark calls whose provider outcome is unknown after a worker crash rather than blindly replaying them.
- Fail over only to an approved route.Pre-approve model behavior, data boundary, region, quality and cost for each fallback. Restarting a streamed answer on another provider needs an explicit user experience.
- Contain provider outages.Use a circuit breaker and bounded recovery rate; do not let every worker probe and retry at once when service returns.
- Expose overload honestly.Return a clear retry time, async state or reduced service when capacity is gone. Silent hanging requests are an architecture failure.
- Instrument the whole request chain.Show quota use, 429s, queue age, retries, fallback rate, duplicate work and successful completions by tenant, journey and provider.
- Plan capacity with the provider.If sustained demand exceeds the contracted quota, secure more capacity or redesign the workload. Retry logic cannot create throughput.
Acceptance criteria
- Quota contract is recorded.The team signs off on limits and approved routes by model, account and region, including burst behavior and provider-specific errors.
- Burst traffic is replayed.Run the agreed interactive and batch mix across short windows as well as a full minute; completed throughput and queue age meet their targets.
- Throttling stays bounded.Inject 429s below the apparent minute quota. The system honors the provider signal, keeps retry volume within the agreed budget and avoids a retry cascade.
- Deadlines are respected.Requests that cannot finish in time go async or fail clearly; none sits indefinitely in the admission queue.
- A persisted result is not paid for twice.Kill a worker after the result is saved but before job acknowledgement; replay uses the saved result and does not repeat downstream side effects. Separately report calls with an unknown provider outcome.
- Provider failure has a known outcome.With the primary route unavailable, the approved fallback meets its quality and latency gate or the product degrades as specified.
- Data boundaries survive failover.Exercise a tenant that forbids an alternate region or provider; no request crosses that boundary.
- Tenants and traffic classes stay isolated.One tenant or batch import consumes its allowance without breaching the live latency target of another.
- Recovery does not stampede.Restore provider service after an outage; queued work drains within the agreed window without another wave of 429s.
- The trace explains every loss.For the test run, show counts of accepted, queued, completed, retried, shed and failed requests, with their token and cost totals.
Retrieval at scale
Retrieval remains fast, fresh and relevant as the corpus and tenant filters grow.
Solution requirements
- Start with your actual query mix.Sample short and long queries, exact IDs, rare filters, broad filters and permission-heavy searches from production traffic.
- Profile every retrieval stage.Separate parsing, embedding, filter evaluation, vector or keyword search, reranking and document fetch before changing the index.
- Choose search by query shape.Benchmark exact, approximate, keyword and hybrid paths where each is useful; do not make every query pay for the same pipeline.
- Filter before exposing results.Apply tenant and document ACLs in the retrieval path. Post-filtering a top-k result can return too few hits or leak candidates to later stages.
- Design indexes for selective filters.Test the combinations users really apply, especially tenant, document type and date; check query plans and hot partitions at projected scale.
- Keep relevance in the latency trade-off.Measure recall and citation correctness on a judged set while tuning k, ANN parameters and reranker depth. Faster wrong results are not a win.
- Treat ingestion as a separate workload.Bound parsing and embedding queues so a bulk import cannot starve live search. Track each document from source event to searchable version.
- Make updates and deletes real.Use stable document IDs and idempotent ingestion. Propagate edits, permission changes and deletions to both source metadata and derived indexes.
- Reindex without a search outage.Version embeddings and index schemas, build the replacement alongside reads and writes, then switch with a reversible cutover.
- Control context assembly.Deduplicate overlapping chunks, keep source links and honor a token budget; excessive retrieved text slows generation and can hide the answer.
- Cache with identity and freshness.Key any result cache by tenant, authorization scope and index version; invalidate it when source access or content changes.
- Watch the tail by filter shape.Report p95 and p99 retrieval time, empty-result rate, freshness lag and relevance by tenant and query segment, not a single average.
- Plan recovery from index loss.Keep source-of-truth data and a tested rebuild or restore path. An index snapshot alone may not recover pending ingestion.
Acceptance criteria
- The corpus matches the next scale step.Seed the agreed document count, size distribution, metadata, tenants and access rules, plus the projected ingestion rate.
- The query set is representative.Replay the agreed mix of broad, highly selective, keyword, semantic and permission-filtered queries at peak concurrency.
- Latency and relevance both pass.p95/p99 retrieval targets hold while recall, answer grounding and citation checks stay above the approved baseline.
- Access never leaks.Cross-tenant, revoked-access and prompt-mediated retrieval attempts return no unauthorized document or snippet, including from caches.
- Freshness is measured end to end.New and changed documents become searchable within the agreed window under simultaneous query load.
- Deletes and ACL changes take effect.A removed document or revoked permission disappears from search, reranking and context assembly within the agreed window.
- Reindexing does not interrupt reads.Build and cut over a new index while writes continue; query success, latency and relevance stay within target, and rollback works.
- Large imports do not starve search.Run ingestion at the agreed peak beside live queries; query latency and ingestion backlog remain bounded.
- Worst queries are explainable.Keep query traces, plans, filter selectivity, reranker time and hot-partition evidence for the slowest segment.
- Recovery is rehearsed.Restore or rebuild a failed index and replay pending changes; verify parity with source-of-truth documents and permissions.
Multi-tenant rollout
New tenants and regions launch through a repeatable, isolated delivery path.
Solution requirements
- Choose the isolation boundary.Document which tenants share application, queue, index, model quota, storage and network resources, and which require dedicated components.
- Carry tenant identity end to end.Propagate authenticated tenant context through APIs, agents, tool calls, background jobs, caches and logs; never trust a tenant ID supplied by the prompt.
- Authorize every data hop.Enforce tenant scope at storage and retrieval, including embeddings, metadata, files and result caches. Cross-tenant checks belong in the service layer.
- Reserve fair capacity.Set per-tenant limits and queue shares for model calls, retrieval, ingestion and agent fan-out so one launch cannot slow everyone else.
- Make placement explicit.Route data, inference, logs, backups and support access to approved regions for each contract. Include subprocessors in the map.
- Automate onboarding.Provision identity, secrets, data stores, limits, observability, retention and default policies from a versioned tenant configuration.
- Avoid customer code forks.Put contractual differences behind controlled configuration and feature flags; keep deployments and upgrades on one maintainable path.
- Roll out per tenant.Canary a new model, prompt, tool or index to selected tenants, with an independent rollback and a record of who changed it.
- Scope operational access.Support and engineering roles need time-bound access and audit records. A global admin key should not be the routine path.
- Measure each tenant.Break down latency, errors, usage, spend, queue age and quality by tenant and region without exposing their content to other tenants.
- Define lifecycle operations.Plan export, deletion, suspension, backup and tenant-level restore before customer count makes these manual jobs unsafe.
- Limit the blast radius.A faulty tenant config, import or rollout should have a bounded failure domain and a known escalation path.
Acceptance criteria
- A new tenant can be launched from configuration.Time the approved onboarding flow with no code fork or ad hoc infrastructure edits; record every manual approval step.
- Cross-tenant access is denied.Test API, retrieval, caches, queues, tools, logs and support workflows with wrong or missing tenant context.
- A noisy neighbor is contained.Drive one tenant beyond its allowance while others run normal traffic; unaffected tenants meet their latency and error targets.
- Regional placement is proven.Trace sample requests, prompts, documents, logs and backups for each residency policy; forbidden routes fail closed.
- Changes can be isolated.Canary a model or index for one tenant, detect a regression and roll that tenant back without affecting others.
- Configuration is repeatable.Recreate a tenant from the versioned definition and detect drift from the approved limits, roles and retention settings.
- Lifecycle actions work.Export, suspend and delete a test tenant; verify derived indexes, caches and scheduled jobs follow the policy.
- Tenant restore is bounded.Restore a tenant from backup without overwriting another tenant or violating its region requirement.
- Operations are auditable.Show who provisioned, changed, accessed and rolled back the tenant, alongside tenant-specific health and usage.
Quality across rollout
Model, prompt, retrieval and data changes keep critical user tasks working.
Solution requirements
- Name the tasks that cannot regress.List the user journeys, tenant segments, languages and failure modes that matter to the business; do not reduce quality to one aggregate score.
- Build a judged baseline.Keep representative real tasks, difficult edge cases and known incidents in a versioned set with expected outcomes and allowed variation.
- Protect private test data.Redact or permission-scope production examples and keep a holdout set out of prompt tuning.
- Version the whole behavior.Pin model, prompt, tool definitions, retrieval settings, knowledge snapshot and runtime configuration to every eval and release.
- Check outcomes, not polished prose.Score task completion, factual grounding, tool correctness, citations and handoff behavior where relevant.
- Use the right grader for each failure.Use exact checks for schemas and hard constraints; calibrate human or model judging for subjective answers against an agreed rubric.
- Measure run-to-run noise.Repeat nondeterministic cases and inspect variance before calling a small score move a real improvement or regression.
- Gate the important slices.Set separate floors for critical tasks and tenants; a strong average cannot hide a broken high-value workflow.
- Include latency and cost.A quality gain that misses the response-time or per-task budget is not ready for broad rollout.
- Canary before broad release.Compare a limited cohort or shadow run to baseline, define rollback triggers and keep the previous working configuration ready.
- Monitor live behavior.Sample production traces, failures, overrides and user feedback with privacy controls; detect silent changes in provider or source data.
- Turn incidents into cases.When a real user finds a failure, add a sanitized reproduction to the regression set and assign an owner for the fix.
- Separate model drift from data drift.Track changes to source documents, query mix and customer cohorts so the team fixes the right layer.
Acceptance criteria
- The baseline is signed off.Critical tasks, rubric, test set version, minimum sample size and per-segment thresholds are agreed before evaluating a candidate.
- Every change is reproducible.The result records exact model, prompt, tools, index and knowledge versions; another engineer can rerun the same candidate.
- Hard failures remain at zero or below the agreed limit.Schema breaks, unauthorized actions, unsupported claims and other critical failures are reported separately from the average score.
- Subjective grading is calibrated.A blind human sample checks the automated judge, and repeated runs show the variance behind pass or fail.
- Critical slices pass independently.Compare high-value tasks, long inputs, languages, tenants and difficult edge cases with their own baseline.
- The holdout has not been tuned away.Run a reserved real-world sample that was not used to edit prompts, retrieval or grading rules.
- A canary has a rollback trigger.Release to an agreed cohort, watch task success, escalations, latency and cost, then roll back automatically or by named owner if thresholds fail.
- Live drift is visible.Show alerts and sampled traces for model, data and query-mix changes after rollout, with an owner for investigation.
- The feedback loop closes.Reproduce a seeded production failure, create a regression case and prove the next release catches it.
- Evidence is reviewable.Keep score breakdowns, failing examples, grader notes, release decision and rollback record instead of only a pass badge.
Unit cost at volume
Cost per successful task remains visible and controlled as usage grows.
Solution requirements
- Use a unit the business recognizes.Track cost per completed task by tenant and workflow, alongside failure and human-handoff rates; price per token alone hides rework.
- Attribute the whole chain.Capture model input and output, embeddings, retrieval, reranking, tools, retries, cache, infrastructure and provider charges under one trace.
- Count failed work too.Include timed-out, abandoned and retried calls in the workflow cost so expensive failures do not disappear from the denominator.
- Watch agent context growth.Record tokens and spend by step; repeated tool calls can resend a growing conversation long before the final answer appears.
- Set hard budgets at several levels.Cap spend, tokens, iterations and tool calls per step, task, tenant and time window; stop a loop before the invoice arrives.
- Estimate before expensive work.Check likely input, output and fan-out against the remaining budget before starting an agent or large batch.
- Route by task, then validate.Use a cheaper model only for journeys where the quality and latency gates still pass; keep an approved escalation path for hard cases.
- Cache only where safe.Use semantic or exact reuse where freshness, privacy and tenant scope permit it, and measure hit rate against incorrect reuse.
- Control context and output.Trim irrelevant history, cap retrieved context and output length, and avoid asking a model to regenerate work the system already knows.
- Schedule elastic work.Move non-urgent batches off the interactive path and use provider pricing modes only when their latency and data terms fit the task.
- Forecast the bad day.Model long-context users, low cache hit, provider fallback, retries and agent loops, not just average tokens times user count.
- Reconcile and alert.Compare metered traces with provider bills and alert on spend per tenant or workflow before a monthly cap is reached.
Acceptance criteria
- The unit-cost formula is agreed.Define successful task, included charges, allocation of shared infrastructure and the target by workflow and tenant.
- Every paid call has an owner.Sample a provider invoice and trace charges back to task, tenant, model, tool and retry within the agreed tolerance.
- Peak mix meets the target.Replay realistic task lengths and tenant mix at projected volume; cost per successful task and success rate stay within their agreed bands.
- Loop caps actually stop work.Inject repeated tool calls, long contexts and nested retries; the task ends or escalates before its hard budget is crossed.
- Failed work stays in the report.Timeouts, aborted sessions and provider errors appear in total spend and in cost per successful task.
- Routing preserves the result.Compare routed and baseline models on the critical eval set, p95 latency and cost; no cheaper route ships solely on price.
- Cache safety is demonstrated.Test permission changes, new documents and cross-tenant lookups; reused output is fresh enough and never crosses a data boundary.
- Fallback has a budget.Simulate provider loss and the more expensive route; tenant and task caps still engage and the forecast shows the impact.
- Spend anomalies are actionable.Create a synthetic bad day and confirm alerts identify the responsible workflow before the agreed spend ceiling.
- Forecast matches accounting.Compare metered monthly projection with invoice and explain the remaining variance by provider, model and shared resources.
Regulated deployment
The deployment fits the buyer’s data boundary and produces evidence for security review.
Solution requirements
- Draw the real data flow.Map prompts, retrieved records, embeddings, tool payloads, logs, traces, caches and backups across every service and provider. Mark PHI or other regulated data where applicable.
- Agree the deployment boundary.Decide which components can run in shared cloud, a private network or the customer perimeter, and which outbound calls are allowed.
- Use enterprise identity.Connect SSO, provision and revoke users, scope RBAC to tenant and role, and use short-lived service credentials where possible.
- Enforce least privilege for agents.Authorize tools and data access outside the model. A prompt should never grant a permission or override an access decision.
- Keep secrets and keys governed.Store credentials in the approved manager, rotate them, encrypt data in transit and at rest, and document key ownership.
- Check every copy and region.Apply residency policy to inference, observability, support, backups and subprocessors, not just the primary database.
- Set retention and deletion paths.Define what providers and internal systems retain, for how long, and how prompts, traces, embeddings and backups are removed or expired.
- Make audit logs useful and safe.Record identities, actions, tool calls, access decisions and config changes with correlation IDs while redacting secrets and sensitive content.
- Control egress and injection paths.Restrict agent tool destinations and test malicious retrieved content, exfiltration attempts and unexpected external calls.
- Assign compliance responsibilities.Map applicable HIPAA or SOC 2 controls to the customer, delivery team, cloud and model providers; track BAAs and other agreements where required.
- Plan incident and support access.Define alert owners, break-glass procedure, evidence preservation, revocation and response steps before launch.
- Keep a reviewable evidence pack.Maintain architecture, threat model, test results, access policy, vendor inventory, retention schedule and change record in one place.
Acceptance criteria
- The security boundary is signed off.Security reviews an accurate data-flow and vendor map with named owners for each applicable control and unresolved exception.
- Access fails closed.Test revoked users, wrong roles, wrong tenants and agent tool calls; none can read or act outside its grant.
- Network routes match policy.Exercise blocked egress and an unavailable approved provider; traffic never silently moves to a forbidden service or region.
- Sensitive data stays out of telemetry.Inspect prompts, traces, errors and support logs for secrets and regulated data according to the approved logging policy.
- Audit events reconstruct an action.From a sampled user or agent action, security can identify identity, tenant, tool, data access, config version and outcome without exposing raw secrets.
- Retention and deletion are tested.Delete a test record and verify the documented path through source stores, indexes, caches, traces and backup expiry.
- Provider terms are checked.For regulated data, required agreements and data-processing settings are confirmed for every service that receives it.
- An attack path is exercised.Test prompt injection and unauthorized tool use against real retrieval and execution boundaries; record and close critical findings.
- An incident drill has evidence.Run a credential compromise or data-exposure scenario and show detection, containment, revocation, escalation and audit preservation.
- The review packet is complete.Hand security the implemented controls, test evidence, vendor responsibilities, open risks and named sign-off owners.
Infrastructure at scale
Cloud capacity, deployments and recovery keep pace with enterprise rollout.
Solution requirements
- Draw the whole workload.Map ingress, app and agent services, model endpoints, queues, search, data stores and observability. Mark which team owns each dependency and where a failure stops the user journey.
- Choose services for the workload.Compare managed Azure or AWS services with containers or self-hosted inference against the actual traffic, data boundary and team capacity. Kubernetes is a choice, not an enterprise-grade requirement.
- Build repeatable environments.Define network, identity, compute, AI services, storage, quotas and policies as versioned infrastructure code. Keep dev, staging and production isolated and detect manual drift.
- Make network paths explicit.Test ingress, private endpoints, DNS, egress and cross-service latency. A healthy model endpoint is useless if the app cannot resolve or reach it.
- Reserve capacity before a rollout.Check model and service quotas, regional SKU availability, GPU or provisioned-throughput lead times, and the capacity held for failover. A scaling policy cannot allocate unavailable resources.
- Scale on useful signals.Use pending work, active requests and service latency alongside CPU or GPU use. Measure node start, image pull, model load and endpoint warm-up before relying on autoscaling for bursts.
- Set a warm baseline.Keep enough ready capacity for the agreed traffic spike and loss of one instance or zone. Decide which batch work can wait when interactive demand rises.
- Separate stateless from durable state.Replicate serving components across failure domains, then identify where queues, indexes, sessions and databases persist state. Define backup, restore and data replay for each.
- Design recovery to the business target.Agree on RTO and RPO for the full journey. Use multi-zone and, where the contract requires it, a tested regional recovery path that includes model and data dependencies.
- Promote one versioned release.Tie infrastructure, application, model configuration and data schema changes to a controlled pipeline. Use canaries or blue-green deployment and plan how to roll back stateful migrations.
- See through the whole stack.Correlate user errors with gateway, DNS, network, queue, inference, storage and cloud service metrics. Alert on the symptom users feel, not only host utilization.
- Make operations ownable.Write runbooks for quota exhaustion, failed deployment, regional service degradation, backup restore and on-call escalation. Name the platform and product owners for each action.
- Track fixed platform cost.Show the cost of warm replicas, standby capacity, private networking and observability alongside variable model spend so the architecture is affordable at the target volume.
Acceptance criteria
- Architecture and ownership are signed off.The team reviews the dependency map, service SLO, RTO/RPO, failure domains and platform-versus-workload responsibilities before implementation.
- Environments are reproducible.Create a clean staging environment from infrastructure code, compare it with production policy, and detect a deliberately introduced manual change.
- The network path works as designed.Test private DNS, endpoint access, egress rules and application-to-model/data latency from the actual runtime, not from an engineer’s laptop.
- Capacity exists before launch.Show approved quotas and available SKUs for the target region and recovery route; prove the workload can add the planned capacity within its deadline.
- Scaling includes cold-start time.Replay the agreed ramp and burst. Measure time from scale signal to a ready instance, including node allocation, image pull and model load, while user-facing targets are checked.
- A zone loss is contained.Remove one serving zone or equivalent failure domain under load. The critical journey remains within its agreed availability and latency limits or follows the documented degraded mode.
- Recovery is practiced, not diagrammed.Restore a durable data service and replay pending work; if regional recovery is required, switch traffic and verify measured RTO and RPO.
- A release can be reversed.Deploy a changed app and model configuration through staging and canary, inject a failure, and roll back without an unplanned outage or incompatible schema state.
- An incident is diagnosable.Inject a DNS, queue or model-endpoint fault. The alert fires within the agreed window and the trace points to the failing dependency and owner.
- The on-call team can run it.An engineer outside the build team follows the runbook to handle a quota or service incident, with access, contacts and recovery steps verified.
Why clients choose Digital Gateway over other embedded teams.
Enterprise-grade starts with the solution you already have. I want to understand what works today, what breaks as demand grows, and what the next stage requires.
From there, we choose the right scaling strategy, assemble the team for that specific job, and take responsibility for how it performs in production.
Denys Matrosov Founder, Digital Gateway
Your outcome is personal to me.
Digital Gateway Resources
Digital Gateway at client projects.
01–03 / 07
Delivery manager
One owner for the plan and the handoffs.
Works with product and engineering leads to keep scope, dependencies, risks, and acceptance visible through the release.
Business & systems analysts
Turn a moving brief into buildable work.
Map workflows with stakeholders, define edge cases and acceptance criteria, and stay close to engineers as requirements change.
Solution architect
Make scaling choices fit the system.
Reviews architecture and constraints with technical leads, maps tradeoffs, and stays involved as the design becomes working software.
Back-end & full-stack engineers
Add build capacity inside the codebase.
Work in product repositories, join code reviews, and follow the release process to deliver APIs and workflows the product team can maintain.
AI & data engineers
Connect models to real data and workflows.
Build agents, document AI, retrieval, and data pipelines with application and data teams, inside established access boundaries.
Evaluation & QA engineers
Know what changed before users do.
Build test cases and eval sets with domain experts, then run regression and release checks against critical user journeys.
DevOps engineers
Give each release an operating path.
Work with platform owners in cloud environments, CI/CD, and monitoring setup to prepare deploys, rollback, and recovery.
AI scale pod
Move one scale target into production.
Joins the AI product team around a defined capacity, latency, or quality milestone, from design through load tests and release.
Enterprise rollout pod
Make the next client deployment repeatable.
Works with platform and security leads on tenant boundaries, identity, auditability, deployment, and handover.
AI integration pod
Put the AI workflow inside the product.
Pairs with application and data teams to connect models, business rules, data flows, exceptions, and evaluation.
End-to-end delivery team
Give a defined product stream one accountable team.
Works with product owners and technical leads across the backlog, architecture, implementation, testing, launch, and support.
Selected project experience
Experience built in demanding environments.
Our work spans financial services, healthcare, logistics, and large-scale enterprise platforms.
What we do
Engineering that carries a project through.
From a new platform to the difficult system inside an existing one, we take responsibility for making the solution work.
Products & platforms
Design and deliver software around the way people, data, and operations actually work.
Integration & modernization
Connect fragmented systems, move critical data, and replace fragile legacy workflows.
Applied AI in production
Put AI into real workflows with evaluation, human oversight, and the controls needed to operate.
12+years shipping software to productiondelivery record
65engineers, analysts & QA at peakdelivery capacity
200 TBof records migrated in one engagementdata scale
200+reference dictionaries in a national registrysystem complexity
Built for production
The work behind a dependable system.
A successful launch is one milestone. The solution must stay useful, observable, and manageable afterward.
Prove the critical paths
Test the workflows, integrations, and edge cases that matter to the people using the system.
Protect data and decisions
Design access, traceability, and review into the system where operational and regulatory requirements demand it.
Launch with visibility
Instrument the service so teams can see failures, measure performance, and improve it safely.
Make the handover work
Document the solution, train users, and support the teams who will operate it.
Selected work
Complex briefs. Working systems.
A selection of problems our teams have helped solve across AI, integration, and large-scale data.
01 / Insurance
Applied AI · workflow
From submissions to structured decisions
AI-assisted intake reads incoming insurance documents, checks extracted data, and routes exceptions to people before downstream systems are updated.
02 / Healthcare
Applied AI · operations
Clinical calls connected to care workflows
Voice agents handle patient conversations and pass useful, reviewable information into care systems, with human intervention where needed.
03 / Logistics
Enterprise integration
One integration layer for many moving parts
A high-throughput service bus connects heterogeneous modules and courier providers, reducing the friction of operating across systems.
04 / Data platform
Migration · modernization
200 TB moved into a long-term platform
Historical records were migrated into a new document storage environment and connected to the workflows needed to keep them useful.
Examples are summarized where client confidentiality limits public detail.
How delivery works
A clear path from brief to adoption.
We work with your technical and business leads to define the outcome, make the hard choices visible, and deliver in usable stages.
Understand the system
Map the users, constraints, existing technology, and result that matters.
Shape the solution
Agree on architecture, critical integrations, risks, and a workable plan.
Build and launch
Ship in stages, validate with real users, and prepare for production.
Support adoption
Monitor, improve, document, and train the teams who depend on it.
Flexible by designOne specialist or a complete delivery function. Fixed scope or ongoing development. The engagement follows the work.
Start a conversation
Bring us the difficult part.
Tell us what needs to work, what is already in place, and where delivery is getting stuck. We will help shape the next step.
GOOD TO KNOW
Frequently asked questions
What does Digital Gateway do?
Digital Gateway designs, builds, integrates and supports high-throughput software and applied AI solutions. The work spans new products, modernization and systems that must operate inside existing business processes.
Who is Digital Gateway for?
We work with CTOs, technical founders and delivery leaders who need a difficult system to work in production. Clients range from growing product companies to organizations with complex enterprise environments.
What do you mean by high-throughput systems?
These are platforms, integration layers and data services designed to handle substantial traffic or data volume while remaining reliable and observable. Our published work includes a 200 TB records migration and national registry platforms.
What kind of applied AI do you deliver?
Examples include voice agents connected to clinical workflows and document intake that validates extracted information before updating downstream systems. We design evaluation, escalation and operational controls around the model.
Do you only build AI products?
No. Platform engineering, systems integration, data migration and modernization are core parts of our work. We use AI where it serves the workflow and the agreed result.
Can you take a project from design through launch?
Yes. We can help frame the solution, design and build it, connect existing systems, launch it, support operations and train end users. The scope is agreed around the outcome.
How do you make a system work after the demo?
We test critical workflows and edge cases, build in access and traceability where needed, instrument the service, stage releases and prepare documentation and handover for the operating team.
Do you work in regulated environments?
Our project experience includes healthcare, insurance, banking and public-sector systems. Controls such as access, data residency and audit trails are designed to fit each client environment; a project is not automatically certified by that experience.
Do clients have to buy an embedded team?
No. An engagement may involve one specialist, a delivery pod or a broader project team. We can work against a defined scope or alongside an ongoing roadmap, depending on the work.
Where is Digital Gateway based and how do we start?
Digital Gateway is led from Vancouver, Canada, with working hours overlapping the United States. Send the goal, constraints and what already exists; we will discuss a practical next step.