Enterprise RAG
Ingestion, hybrid retrieval and grounded generation engineered for real enterprise data.
Services
AI Infrastructure Services
The foundations that make AI trustworthy in production — retrieval, evals, monitoring, and safety.
- 90%+retrieval accuracy on golden sets
- 100%of AI calls traced and scored
- <50msguardrail overhead per interaction
Capabilities
The foundations every AI system needs
RAG, knowledge engineering, evaluation, observability, memory systems and guardrails.
Evaluation and testing
Golden datasets, CI gates and production scoring that make quality measurable.
Observability
End-to-end tracing, quality monitoring and cost intelligence for AI systems.
Guardrails and safety
Runtime policy enforcement, red-teaming and audit trails.
How we deliver
Build the platform once, ship use cases forever
Teams that skip infrastructure rebuild it badly inside every project. We build it properly once — and every agent, copilot and workflow after inherits it.
Knowledge layer
Retrieval that survives your real documents
Scanned PDFs, permission sprawl, six versions of every policy — we engineer ingestion, hybrid search and reranking against that reality, validated by retrieval evals.
- Parsers and chunking tuned per content type
- Hybrid vector + keyword search with reranking
- ACLs enforced inside the query, not after
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Quality layer
Evals as the operating system of change
Golden sets from your real traffic, LLM-judge and programmatic scoring in CI, and production sampling — so every change ships with evidence and drift gets caught early.
- Golden datasets mined from production reality
- Regression gates on every prompt and model change
- Continuous scoring of live traffic samples
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Trust layer
Observability and guardrails as standard
Full tracing with cost attribution, and policy enforcement outside the model — injection defense, PII redaction, action gates — engineered in from the first sprint.
- OpenTelemetry-native tracing across the AI path
- Cost attribution per feature and team
- Runtime policy engine with immutable audit logs
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Infrastructure engagements we run
RAG platform builds
Shared retrieval serving search, copilots and agents.
Eval program setup
From zero to CI-gated releases in weeks.
Observability retrofits
Existing AI systems instrumented end-to-end.
Guardrail hardening
Red-team, then layered runtime defenses.
RAG rescue
Disappointing retrieval diagnosed and rebuilt.
Platform consolidation
Four team stacks unified into one governed layer.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
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1
Align
Goals, constraints, success metrics and stakeholders.
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2
Design
Architecture, delivery plan and risk controls.
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3
Deliver
Build, integrate, harden and launch.
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4
Operate
Monitor, optimize cost and continuously improve.
We had three teams with three RAG stacks and no way to say which was better. Now there's one platform, one eval harness, and quality arguments end with a scoreboard.
Works with your stack
Proven components, your cloud
Best-of-breed infrastructure assembled in your environment — no proprietary lock-in.
- Pinecone
- Weaviate
- pgvector
- Elasticsearch
- Unstructured
- LangSmith
- Braintrust
- OpenTelemetry
- Datadog
- Presidio
- AWS
- Kubernetes
FAQ
Common questions
Should infrastructure come before or after the first use case?
With it. We build the first use case on thin, correct foundations — real evals, real tracing — then harden and generalize. Infrastructure-first without a use case produces shelfware; use-case-first without foundations produces rewrites.
Our RAG disappoints. Rebuild or tune?
Diagnose first: retrieval metrics usually reveal the culprit — chunking, search strategy or missing evals — within days. Most systems need targeted rebuilding of one layer, not a restart.
Can you instrument systems you didn't build?
Yes — observability and eval retrofits on existing stacks (LangChain, custom, vendor) are a standard engagement, typically landing within a sprint or two.
What do we own at the end?
Everything: code, configs, dashboards and eval sets in your repositories, documented and handed over. We optimize for your independence.
Next step
Give every AI project solid ground
Start with an infrastructure assessment — we'll show you exactly where the gaps are.