What We Build

Five practice areas. Each scoped, built, evaluated, and handed off by the same team that designed it.

1 Retrieval & Knowledge

RAG System Architecture

Retrieval systems that stay accurate under real-world data conditions — with hybrid search, grounding controls, and evaluation built in.

What we build
  • Ingestion and chunking strategy for mixed content
  • Hybrid search with metadata-aware retrieval
  • Citation and grounding controls
What improves
  • Every response cites where it came from — your team trusts the answers
  • New knowledge sources onboard in hours, not weeks
  • Hallucination rate drops with a measurable baseline
Assess my retrieval setup
2 Workflow Automation

AI Agent Development

Agent systems with explicit boundaries and controls so automation stays reliable as it scales.

What we build
  • State-aware agent workflows with tool orchestration
  • Approval checkpoints for high-stakes actions
  • Retry, fallback, and incident pathways
What improves
  • Repeatable workflows complete without manual intervention
  • Every automated action has a defined approval path
  • Runtime costs stay predictable as volume scales
Scope an agent workflow
3 Behavior Optimization

Prompt Engineering & Optimization

Turn ad hoc prompting into a governed system with versioning, evaluation, and safe release workflows.

What we build
  • Prompt libraries with role and policy templates
  • Evaluation sets and scoring pipelines
  • A/B testing workflows for prompt releases
What improves
  • Prompt releases ship with test coverage — no surprise regressions
  • Any engineer on the team can modify behavior safely
  • Quality scores are tracked across teams over time
Review my prompt system
4 Model Adaptation

Fine-Tuning & Model Adaptation

When prompt and retrieval gains plateau, we design data-centric training workflows with clear economics.

What we build
  • Training dataset curation and quality filtering
  • Experiment tracking and benchmark suites
  • Deployment with rollback-safe model versioning
What improves
  • Domain accuracy reaches levels prompting alone cannot
  • Training investment is justified before a single GPU runs
  • Model versions deploy with rollback safety built in
Evaluate fine-tuning fit
5 Reliability

LLM Evaluation & Production Reliability

Observability and governance to keep AI systems stable as they evolve in production.

What we build
  • Continuous quality and drift evaluation pipelines
  • Latency, cost, and error observability dashboards
  • Operational playbooks for incident response
What improves
  • Quality regressions surface before users find them
  • Every team shares a single, objective quality score
  • Operating costs are visible, attributed, and controllable
Audit my production stack

From First Workshop to Internal Ownership

Every engagement follows four phases with clear outputs and decision checkpoints.

1

Diagnose

Map target workflows, bottlenecks, and baseline metrics to scope the right intervention.

2

Architect

Define data flow, model strategy, interfaces, and governance before implementation begins.

3

Implement

Ship weekly increments with measurable outcomes and controlled rollout to production.

4

Transfer

Deliver documentation, runbooks, and team enablement so your organization owns the system.

Need a specific delivery plan?

We can scope your first milestone with concrete outputs, timeline, and decision checkpoints.

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