LLM INTEGRATION · RAG PIPELINES
LLM systems built for reliability, evaluation, and governance.
We implement LLM capabilities as production systems—not prompt demos. Retrieval is designed for grounding and freshness, quality is measurable via evaluation harnesses, guardrails enforce policy, and cost/latency are controlled with routing and budgets. The result is predictable behavior you can operate and improve over time.
THE SYSTEM
An LLM capability you can measure, govern, and improve.
LLM projects fail when quality can’t be measured, grounding isn’t enforced, and costs drift as usage grows. We treat LLM integration as an engineering system with explicit inputs, evidence, and evaluation.
That means retrieval-first grounding, test sets and release gates, policy guardrails, and telemetry for quality, latency, and cost—so behavior stays predictable as data and usage evolve.
EXECUTION DISCIPLINE
Predictable behavior requires evaluation and guardrails.
We ship LLM systems with grounding, measurable quality, and governance—so you can trust outputs, control risk, and scale usage responsibly.
01
Grounding via retrieval
We design chunking, retrieval, reranking, and freshness policies so outputs are anchored in approved sources—not model memory.
- Sources are allowlisted and access-controlled.
- Freshness policy is explicit (reindexing, backfills, content lifecycle).
- Citations/evidence are available where the workflow requires trust.
02
Evaluation as a release gate
We implement repeatable evaluation with test sets and scoring rubrics so changes don’t silently regress quality.
- Quality targets are defined per workflow (groundedness, correctness, refusal behavior).
- Regression checks run before deploying prompt/model/retrieval changes.
- Drift is monitored in production with actionable signals.
03
Governance, safety, and cost control
We enforce policy guardrails, redaction rules, and budget envelopes so enterprise usage stays safe and predictable.
- Prompt injection defenses and tool allowlists protect actions.
- PII handling and logging policies are designed and auditable.
- Token/cost/latency budgets are monitored and enforced per route.
ARTIFACTS & OUTCOMES
LLM capability grounded in your knowledge and governed in production.
You get RAG architecture, evaluation harnesses, safety policies, and observability—so quality and trust improve over time instead of drifting.
RAG system blueprint
Chunking strategy, retrieval/reranking design, freshness policy, and system contracts.
Evaluation harness + test sets
Repeatable evaluation, regression tracking, and quality targets aligned to real user tasks.
Guardrails + governance
Prompt injection defenses, PII redaction policies, and controlled tool-calling boundaries.
Observability + audit trails
Telemetry for latency, quality, and costs—plus logging policy that fits enterprise constraints.
Cost/latency envelope
Budgets, caching, routing strategies, and monitoring to keep usage predictable at scale.
Production integration
API interfaces, auth boundaries, and deployment strategy so the LLM system is operable end-to-end.
OPERATING QUESTIONS
The questions that separate demos from production LLM systems.
We answer how grounding works, how quality is measured, what guardrails exist, and how cost/latency are controlled at scale.
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Other services
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