Judgment and escalation
Domain owners define material decisions, approval thresholds, and when a human must take over.
Engineering Service
AI Platform Engineering for Governed Production Systems
We engineer the data, evaluation, security, observability, model-routing, and operating controls that turn an AI capability into a system an enterprise can release and defend.
Talk to an AI Platform Engineer →Production architecture
An AI model is one component. The platform is the control system around it.
Production AI has to remain useful when inputs are ambiguous, models change, dependencies fail, and a decision is challenged later. The system path below makes those operating constraints visible.
This is usually where a convincing prototype becomes a consequential engineering system.
- 01RequestUser, system, or agent intent enters a defined trust boundary.
- 02AuthorizeIdentity, purpose, data scope, and tool permissions are checked.
- 03GroundApproved sources are retrieved through versioned data contracts.
- 04RouteThe workload is sent to an evaluated model and fallback path.
- 05EvaluateTask quality, policy, safety, and regression checks gate release.
- 06ActOnly authorized tools can change an external system.
- 07EvidenceInputs, decisions, versions, approvals, and outcomes remain reviewable.
Show the production controls
Evaluation and release control
Versioned datasets, acceptance thresholds, red-team cases, rollback criteria, and incident procedures govern change.
Runtime enforcement
Authorization, model routing, retries, observability, fallbacks, audit events, and data boundaries execute in software.
Production AI X-Ray
The model is not the production system.
Reveal the engineering layers that turn model capability into bounded, observable workflow behavior.
Select an evaluated model and an explicit fallback path.
Ground work in approved, versioned sources.
Authorize data and tools for the purpose of the request.
Gate behavior against quality, policy, and safety cases.
Degrade safely when a model or dependency fails.
Stop and route material exceptions to an accountable person.
Trace latency, cost, errors, and task outcomes.
Retain reviewable decisions, versions, and approvals.
Restore a known release when evidence crosses a threshold.
Context changes architecture
One capability, different control boundaries.
Production AI architecture changes with the decision consequence, data boundary, regulator, and recovery requirement. The platform must keep model choice separate from authorization, retrieval, evaluation, action, and evidence.
Healthcare and life sciences
Clinical context, protected data, human accountability, interoperability, and safe degradation shape the system.
Explore context →Financial services and insurance
Decision traceability, customer-data boundaries, model governance, segregation, and resilience shape release.
Explore context →Government and public sector
Public accountability, sovereignty, accessibility, records, and procurement constraints expand the control boundary.
Explore context →Energy and critical infrastructure
Constrained environments, operational safety, cyber boundaries, and recovery requirements govern where AI may act.
Explore context →Buyer context
What has to be true before this investment works.
AI platform engineering turns separate models, data sources, prompts, tools, and experiments into a governed production capability. The buying decision concerns the whole operating system: how workloads ingest data, retrieve context, route models, evaluate changes, enforce permissions, escalate to people, control cost and latency, survive dependencies, and produce reviewable evidence.
Problems behind the search
Visible symptoms, technical causes, and the decision to make.
Experiments cannot cross the production boundary
- What the buyer sees
- A notebook or assistant performs well, but security review, data access, integration, evaluation, or operations blocks release.
- What causes it
- The model was selected before the workload boundary, data contracts, permissions, failure behavior, and accountable owner were designed.
- What to evaluate
- Evaluate the end-to-end architecture and one production-shaped workflow, not model accuracy in isolation.
RAG answers are plausible but untrustworthy
- What the buyer sees
- Answers omit relevant sources, cite stale passages, cross entitlements, or change when documents are re-indexed.
- What causes it
- Ingestion, chunking, metadata, retrieval, reranking, citations, freshness, and authorization are unmeasured or disconnected.
- What to evaluate
- Require retrieval and answer evaluations, source lineage, entitlement filtering, deletion propagation, and missing-evidence behavior.
Every model change is an operational gamble
- What the buyer sees
- A provider, model, prompt, embedding, or policy update improves one task while silently breaking another.
- What causes it
- Versions are not bound to representative evaluations, staged release, outcome monitoring, and rollback.
- What to evaluate
- Inspect the evaluation registry, release thresholds, shadow or canary path, change lineage, and safe fallback.
AI cost and latency grow without control
- What the buyer sees
- Long context, repeated agent steps, retrieval fan-out, and premium models create unstable unit economics and response time.
- What causes it
- Routing and budgets were added after the workflow rather than designed around task value and service objectives.
- What to evaluate
- Evaluate task-level cost, latency budgets, caching safety, routing, step limits, capacity, and degradation.
Architecture depth
The design decisions underneath the outcome.
Ingestion and data contracts
Validate source identity, schema, classification, owner, freshness, quality, retention, residency, and permitted use before material enters training, retrieval, feature, or evaluation paths. Quarantine failures and make stale state visible.
RAG and vector retrieval
Preserve document version, entitlement, metadata, chunk lineage, embedding version, deletion, and citation. Evaluate recall, ranking, answer grounding, and refusal across representative and adversarial queries.
Model routing and orchestration
Route by capability, risk, latency, residency, availability, and cost. Keep provider adapters, typed outputs, tool execution, state, retry, and human escalation separate enough to test and replace.
Evaluation and release management
Version datasets, prompts, policies, retrieval configuration, tools, and models together. Gate releases on task, safety, authorization, latency, cost, and failure outcomes; stage traffic and preserve rollback.
Permission and governance plane
Propagate user, workload, tenant, purpose, and data policy through retrieval and tools. Record policy decisions, approvals, exceptions, model lineage, and material actions without turning sensitive content into broad telemetry.
Observability and human escalation
Trace outcome, dependency health, retrieval quality, model response, tool state, token and compute cost, queueing, and operator intervention. Provide kill paths, durable handoff context, and runbooks for degraded operation.
Private and sovereign deployment
Place models and platform services across customer VPC, dedicated, hybrid, on-premises, or sovereign environments according to data, residency, latency, control, provider, and operating requirements. Location alone does not establish governance.
MLOps and model lifecycle
Govern training or adaptation data, artifacts, evaluation, approval, registry, deployment, monitoring, retraining, retirement, and reproducibility. Foundation-model use still requires lifecycle control for the versions and configurations the system actually runs.
Material use cases
Where the system fits, and where people remain accountable.
Enterprise retrieval platform
Ingest approved sources once and provide permission-aware retrieval, citations, evaluation, and monitoring to multiple bounded applications.
Human accountability. Source and application owners govern content and consequential use.
Engineering constraints. Entitlements, tenancy, freshness, deletion, indexing versions, malicious content, and shared-platform blast radius.
Regulated decision support
Assemble evidence, apply deterministic policy, invoke a bounded model, and route a reviewable recommendation to the accountable professional.
Human accountability. The authorized professional or rules process owns the decision.
Engineering constraints. Intended use, explanation, representative evaluation, prohibited data, version evidence, appeal, and drift.
Enterprise agent platform
Expose governed model routes, memory, tools, MCP servers, evaluations, approvals, and tracing to product teams.
Human accountability. Platform teams own shared controls; product teams own workflow risk and outcomes.
Engineering constraints. Tool authorization, tenant isolation, platform exceptions, runaway execution, model supply, and cost allocation.
Private AI workload
Deploy inference, retrieval, orchestration, and observability within a customer-controlled network and data boundary.
Human accountability. Customer security and platform owners accept the deployment and operating model.
Engineering constraints. Model availability, accelerators, patching, key custody, egress, residency, capacity, and lifecycle operations.
Implementation sequence
From system truth to an operated release.
- 01
Profile workloads and deployment constraints
Map models, data, residency, latency, cost, availability, user decisions, and private or sovereign deployment needs.
- 02
Establish data and retrieval contracts
Define ingestion, provenance, chunking, entitlements, freshness, lineage, quality, and deletion.
- 03
Build routing and orchestration controls
Implement model selection, tool boundaries, durable state, budgets, fallback, approval, and provider isolation.
- 04
Create evaluation and release infrastructure
Version prompts, models, retrieval, tools, and datasets with task, safety, latency, and cost thresholds.
- 05
Instrument production failure modes
Trace stale retrieval, hallucination, authorization mismatch, drift, dependency failure, spend, and escalation.
- 06
Deploy a bounded production workload
Run shadow and canary stages, validate rollback and residency, then transfer lifecycle ownership.
Failure modes
How production breaks, and what the architecture must do next.
Stale or poisoned retrieval
Signal. Answers rely on superseded, unauthorized, or hostile source content.
Architecture response. Validate ingestion, retain source and version lineage, filter entitlements before retrieval, evaluate adversarial content, and expose freshness.
Model or provider degradation
Signal. Schema compliance, refusal, latency, availability, or task outcomes change.
Architecture response. Detect at the task and provider layers, stop rollout, route only to evaluated fallbacks, preserve durable state, and escalate when no safe path exists.
Runaway cost or latency
Signal. Context, retries, queues, or agent steps exceed the workload budget.
Architecture response. Use budgets, model routing, context control, step limits, caching where safe, backpressure, circuit breakers, and an explicit incomplete result.
Permission failure
Signal. A retrieval or tool path grants more access than the requesting principal and purpose.
Architecture response. Deny at the resource and action layer, test cross-tenant and privilege-change cases, revoke derived access, and contain the affected path.
Evaluation blind spot
Signal. Offline tests pass while users experience new failure modes or changed data.
Architecture response. Connect production feedback and incidents to curated evaluation cases, segment outcomes, monitor drift, and require review before expanding scope.
Buyer evaluation
Questions to resolve before selecting an approach.
- Which production workloads justify shared platform capability?
- How are data, prompts, models, tools, policies and evaluations versioned together?
- Can every retrieval and action enforce the user’s current permissions?
- What are the task-level latency and cost budgets?
- Which fallback is already evaluated for each failure?
- Can the platform run in the required residency and network boundary?
- How does a person stop, inspect, assume, and resume a workflow?
Buyer questions
Frequently asked before an engineering engagement.
Can we deploy AI inside our own cloud or VPC?
Yes. Inference, retrieval, orchestration, tools, evaluation, and telemetry can run in a customer-controlled VPC or hybrid environment. The design must still address model supply, keys, egress, administrators, patching, capacity, data use, monitoring, and lifecycle ownership.
What is the difference between an AI application and an AI platform?
An application delivers one workflow. A platform supplies reusable model access, ingestion, retrieval, evaluation, policy, identity, observability, release, and cost controls to multiple workloads. Build shared capability only where reuse and governance outweigh platform complexity.
Do we need a vector database for RAG?
Not always. Keyword, relational, graph, or hybrid retrieval may fit the corpus and questions better. Choose from measured retrieval quality, filters, update and deletion behavior, latency, scale, operability, and source semantics rather than assuming vectors are the architecture.
How do you roll back an AI release?
Bind the model, prompt, retrieval, policy, tool, and data versions; preserve the prior evaluated configuration; stage traffic; and ensure state compatibility. Rollback may require re-indexing, replay, or forward correction, so it must be rehearsed before release.
How do you control AI platform costs?
Measure unit cost by workflow and outcome, constrain context and agent steps, route models by task, cache only when authorization and freshness permit, manage accelerator capacity, and stop work that exceeds a bounded value or budget.
Continue the technical investigation
Related services, practices, knowledge, and proof.
Federated population-health analytics
Governed analytics across hospital data boundaries.
Explore this resource →Primary-care workflow automation
AI assistance embedded in an accountable clinical workflow.
Explore this resource →Remote cardiac monitoring
Production AI and alerting around continuous cardiac data.
Explore this resource →Evidence publication standard
How public outcome claims are reviewed before publication.
Explore this resource →AI & Agentic Systems
Architecture guidance for agent workflows and deterministic boundaries.
Explore this resource →Data & AI Platforms
Data contracts, lineage, quality, and serving.
Explore this resource →Agentic AI Engineering
A commercial path for a bounded agent workflow.
Explore this resource →Related architecture and technical context
Industries
Industries We Support
Healthcare
Healthcare — Hospitals & Health Systems
Engineering teams that understand clinical reality
AI Platform Engineering for Healthcare →
Healthcare
Healthcare — Payers & Insurance
Claims intelligence without the compliance anxiety
AI Platform Engineering for Healthcare →
Healthcare
Healthcare — Pharmaceuticals & Life Sciences
FDA-grade engineering for clinical and commercial systems
AI Platform Engineering for Healthcare →
Healthcare
Healthcare — Digital Health & Telemedicine
Scale fast without the compliance debt
AI Platform Engineering for Healthcare →
Financial Services
Financial Services — Banking
Core systems that don't hold you hostage
AI Platform Engineering for Financial Services →
Financial Services
Financial Services — Fintech
Move fast and stay compliant
AI Platform Engineering for Financial Services →
Government
Government & Public Sector
Fixed-price delivery. Working systems. No discovery phase.
AI Platform Engineering for Government →
Energy
Energy & Utilities
Critical infrastructure deserves critical engineering
AI Platform Engineering for Energy →
Telecommunications
Telecommunications
Transform without the transformation theater
AI Platform Engineering for Telecommunications →
Methodology
How We Move From Evaluation to Controlled Release
Our AI teams come domain-qualified. They understand your regulatory landscape before they write their first line of code. Compliance is enforced automatically through ALICE at every commit.
AI Platform Engineering Capabilities
—Custom AI/ML system development
—Compliance-native architecture
—Multi-model orchestration
—Real-time inference infrastructure
—Model monitoring and governance
—Regulatory audit trail automation
Our standard
✓Named engineering ownership and explicit delivery boundaries
✓Applicable controls established with accountable customer owners
✓Production-shaped validation before material release
✓Source, runbooks, and operating knowledge included in handoff scope
✓Recovery and escalation designed to match system consequence
Regulatory
Relevant Compliance Frameworks
HIPAASOC 2GDPRUAE PDPLNIST AI RMFFDA 21 CFR Part 11
Structure
Engagement Models
Geography
Where We Deploy
US
United States
Headquarters / Colorado
UK
United Kingdom
Operations / London
IN
India
Engineering Center / Indore
UAE
UAE & Gulf
Serving the Gulf Region
ANZ
Oceania
Serving Australia & New Zealand
Northeast / New York MetroMid-Atlantic / DC MetroSoutheast / AtlantaFloridaMidwest / ChicagoTexas / Dallas-HoustonMountain West / Denver-ColoradoPacific Northwest / SeattleCalifornia / Bay AreaCalifornia / Los AngelesLondon & SoutheastMidlandsNorth England / Manchester-LeedsScotland / EdinburghWalesNorthern IrelandDubaiAbu DhabiSaudi Arabia / RiyadhSaudi Arabia / NEOMQatar / DohaBahrainOmanSydney / New South WalesMelbourne / VictoriaQueensland / BrisbanePerth / Western AustraliaNew Zealand / Auckland-Wellington
DECISION GUIDE
Build vs. Outsource Decision Framework
A structured framework — with scoring — for deciding whether to build in-house, outsource, or adopt a hybrid model. Adapted for regulated industries where the cost of the wrong decision is highest.
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Our engineers understand your domain before they write their first line of code. Production AI for regulated environments.
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Industry
Healthcare — Hospitals & Health Systems
Industry
Healthcare — Payers & Insurance
Industry
Healthcare — Pharmaceuticals & Life Sciences
Industry
Healthcare — Digital Health & Telemedicine
Related Service
Compliance Infrastructure
Related Service
Healthcare Technology
Related Service
Data Engineering & Analytics
Knowledge Base
Agentic Ai
Knowledge Base
Llm Ops
Knowledge Base
Rag Pipelines
Knowledge Base
Nist
Solution
Failed Vendor Recovery
Solution
Compliance Remediation
Engagement
Surgical Strike (Tier I)
Engagement
Enterprise Program (Tier II)
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