Enterprise AI architecture, built to hold weight.
Vendor-neutral architects who design your AI platform, agents and governance, then build them alongside your team.
Most enterprise AI stalls between pilot and production. The gap is rarely the model.
It is the architecture around the model: the data, the platform, the controls and the ownership that production demands and a demo never tests. A shared enterprise AI architecture gives every team those layers once, instead of rebuilding them per pilot.
Data the model cannot trust
Pilots run on a clean extract. Production runs on the real estate: stale, duplicated, and permissioned in ways nobody documented.
No front door to models
Every team wires its own API keys, prompts and guardrails. Cost, risk and quality become impossible to see, let alone manage.
Security added at the end
Prompt injection, data leakage and over-privileged agents surface in the security review, weeks before launch, and stop it.
Nobody owns it after launch
Without evaluation, monitoring and a named owner, quality drifts quietly until a user finds the failure for you.
The stack we design, floor by floor.
Every production AI system we build rests on the same five floors and one structural core. Where you are weakest is where we start.
- ExperienceCopilots and assistantsCustomer channelsAI inside core appsWhere people meet the system. Designed around the job, with clear hand-offs to humans.
- Agents and orchestrationAgent runtimeTool and MCP gatewayHuman approval stepsAgents that act through governed tools, with durable workflows and checkpoints you control.
- Models and gatewayLLM gateway and routingGuardrailsEvaluation harnessOne front door to every model, with policy, cost attribution, fallbacks and evals built in.
- Knowledge and retrievalIngestion pipelinesHybrid search indexPermission-aware retrievalAnswers grounded in your content, filtered by what each user is allowed to see.
- Data foundationLakehouse or warehouseData contractsLineage and qualityTrustworthy, governed data products that AI workloads can depend on.
- Identity
- Policy
- Audit trail
- Observability
- Cost control
Ten services. One architecture.
All servicesStrategy and operating model
- AI Strategy and RoadmapA use-case portfolio, target architecture and sequenced roadmap that turns AI ambition into funded, buildable work.
- AI Readiness AssessmentA fixed-scope review of data, platform, security, governance and skills, scored against the use cases you actually plan to run.
- AI Operating Model and Center of ExcellenceOperating model, AI center of excellence, architecture review board and intake process designed so teams ship AI faster and safely.
Platform architecture
- Enterprise AI Reference ArchitectureA vendor-neutral reference architecture for models, retrieval, agents, data and controls that turns one-off pilots into a reusable platform.
- Generative AI Platform ArchitectureLLM gateway, model routing, evaluations and cost controls that let teams use multiple models safely, observably and within budget.
- RAG and Knowledge ArchitectureRetrieval-augmented generation for enterprise content: access control, citations, freshness and answer quality you can measure.
- Agentic AI ArchitectureArchitecture for AI agents that use tools and take actions: identity, MCP, orchestration, guardrails and human-in-the-loop controls.
- Data Architecture for AIData foundations for AI workloads: unstructured data pipelines, semantic layers, lineage, quality and access control for models and agents.
Governance, risk and security
- AI Governance and RiskAI governance aligned to NIST AI RMF, ISO/IEC 42001 and the EU AI Act: inventory, risk tiers, model risk and evaluation that work in practice.
- AI Security ArchitectureSecurity for LLM apps and agents: threat modeling, prompt injection and data leakage controls, agent identity and red teaming.
Assess. Blueprint. Build. Govern.
Each stage stands on its own and ends in something you keep. Start where the risk is highest.
Our approach in detail
Assess
WeeksWe map your use cases, data, platforms and controls, and find the decisions that are actually blocking production.
Blueprint
WeeksA target architecture, a sequenced roadmap and a decision log your engineers and risk teams can both sign.
Build
MonthsOur architects work inside your team to ship the first production workloads on the new foundation.
Govern
OngoingArchitecture authority, evaluations and review boards that keep quality, cost and risk visible as you scale.
Built for industries where the controls are not optional.
- Financial ServicesGoverned AI platforms for banks, lenders and asset managers, designed to clear model risk review and produce the records examiners ask for.
- InsuranceAI platforms and governance for carriers, MGAs and brokers, built around the NAIC Model Bulletin, unfair discrimination rules and claims realities.
- Healthcare and Life SciencesPHI-aware AI platforms and clinical governance for health systems, payers and life sciences, built for HIPAA, FDA oversight and patient safety.
- Public SectorAuthorizable AI platforms and governance for federal, state and local agencies, designed around FedRAMP, NIST 800-53, CJIS and current OMB guidance.
- ManufacturingOT-aware AI architecture for manufacturers: plant data foundations, maintenance and quality AI, engineering knowledge systems and export-controlled data handling.
- Legal and Professional ServicesMatter-aware AI architecture for law firms, legal departments and professional services, built around confidentiality, privilege, ethical walls and ABA guidance.
- Technology and SaaSMulti-tenant AI architecture for software and SaaS companies: tenant isolation, agent features, evaluation pipelines, unit economics and enterprise security review.
Latest thinking
All insightsHow Much Does Enterprise AI Architecture Consulting Cost?
An hourly rate tells you almost nothing about what an enterprise AI engagement will cost. Scope, seniority, risk and what you keep at the end do.
ReadStrategyHow to Choose an Enterprise AI Consulting Partner
The right AI consulting firm leaves you with systems in production and a team that can run them. Here is how to tell which firms can do that before you sign.
ReadGovernance and riskNIST AI RMF Implementation Guide: From Framework to Working Program
The NIST AI Risk Management Framework is voluntary, flexible and short on prescriptions. That is its strength and the reason most teams stall. Here is how to turn it into a program that runs.
ReadQuestions we hear
What does an enterprise AI architecture firm do?
An enterprise AI architecture firm designs the systems that let AI run safely at scale inside a large organization: the model platform, retrieval and agent layers, the data foundation, and the governance and security controls around them. We then build the first production workloads with your engineers so the design is proven, not theoretical.
How are you different from a large consultancy or systems integrator?
You work directly with senior architects for the whole engagement, not a partner at kickoff and a junior team after. We are vendor-neutral and resell nothing, so our recommendations are not shaped by partner incentives. And we design for handover: your team owns the architecture, the code and the decision log when we step back.
Do you partner with or resell specific AI vendors?
No. We work across Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, OpenAI and open-weight models, and across data platforms such as Databricks and Snowflake. We recommend what fits your constraints, and we document why, so you can revisit the choice as the market moves.
What does a typical engagement look like?
Most clients start with a fixed-scope Architecture Assessment that runs a few weeks and ends in a clear set of findings and priorities. From there we produce a Blueprint, then build alongside your team, and some clients keep us on as an ongoing architecture authority. Each stage stands on its own, so there is no long commitment up front.
Can you work with our existing engineering team and platforms?
Yes, and we prefer to. We design around the cloud, identity, data and security platforms you already run, and we embed with your engineers rather than building in a separate room. The goal is a platform your people understand and can extend without us.
Who owns the architecture and the code?
You do. Architecture documents, decision records, infrastructure code and application code produced in an engagement belong to the client. We avoid proprietary frameworks for exactly this reason: nothing we build should require us to keep running.
Bring us the architecture question you are stuck on.
Kelsey, our AI assistant, can answer questions and book you a 30-minute briefing with a senior architect in about two minutes, by voice or by typing.
- Vendor-neutral advice. We resell nothing.
- Senior architects on the call, not a sales team.
- Mutual NDA available before we talk specifics.
Book with Kelsey
She finds a time that works, books it on our calendar and sends the invite. No forms.
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Prefer a person? Call (301) 301-5765.