Engineering

What We Build

We design and implement the technical layer that makes AI reliable in production: data pipelines, LLM integration patterns, evaluation harnesses, and the operational tooling teams need to run AI systems confidently over time.

Core Capabilities

LLM Pipeline Architecture Versioned prompts, structured output validation, retry logic, and audit logging — the building blocks that separate a demo from a dependable system.

Evaluation & Quality Assurance Automated eval harnesses, golden test sets, and LLM-as-judge scoring. Every model change is measured against a defined baseline before it reaches production.

Observability Custom instrumentation for token consumption, latency distribution, and output quality. You get dashboards that answer operational questions, not just error counts.

Integration Engineering Clean, documented integrations with your existing data infrastructure: ERP systems, CRMs, data warehouses, and internal APIs. No lock-in.

Our Engineering Standards

• All production prompts are version-controlled and change-reviewed
• Output schemas are validated at runtime — no silent failures
• Deployments include a rollback path
• Documentation is a deliverable, not an afterthought

When this work is useful

Engineering work starts when a prototype produces useful answers but cannot yet be trusted by the system around it. Common triggers are inconsistent JSON, prompts edited in a console, retries that multiply cost, or an integration that has no clear rollback path. The question is not whether a model can answer one example; it is whether the complete workflow has observable contracts.

Delivery model

We begin with one representative workflow and a baseline set. Inputs, outputs, latency expectations, access boundaries, and human approval points are written down before implementation. The first slice then connects the real data boundary to a versioned prompt and a validated output schema. Subsequent slices add retries, fallbacks, traces, and deployment controls only where the workflow needs them.

Typical deliverables include a system map, prompt registry, schema definitions, evaluation harness, integration adapters, operational dashboard, runbook, and rollback procedure. Related patterns are documented in the LLMOps service and the production pipeline insight.

Dependencies, limits, and acceptance

The work depends on stable access to representative data, an owner for the downstream process, and an environment in which logs and secrets can be handled deliberately. A model cannot compensate for missing source data or an undefined business decision. Acceptance is therefore based on agreed cases: schema pass rate, critical factual checks, permitted failure behaviour, latency budget, and a documented human escalation path. No system is considered done merely because a demo responds convincingly. Start with a readiness conversation.

Integration notes

The implementation stays close to the existing stack. Adapters isolate provider calls from business logic, typed contracts make changes reviewable, and explicit state keeps retries and partial completion visible. Where a workflow has a human gate, that gate is represented in the data model and not left to an unwritten convention. This keeps the first release small while preserving a path to additional sources, models, or interfaces.