How It Works

From discovery to production in weeks, not months. Our battle-tested process ensures you ship AI that works.

1

Discovery & Intake

We start with a deep-dive session to understand your use case, data sources, existing infrastructure, and success criteria.

Deliverables:

  • ✓Technical requirements doc
  • ✓Architecture proposal
  • ✓Timeline and milestones
🎯
Step 1 of 5
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Step 2 of 5
2

Proof of Concept

In 1-2 weeks, we build a working prototype that proves the concept and identifies technical risks early.

Deliverables:

  • ✓Working PoC
  • ✓Performance benchmarks
  • ✓Risk assessment
3

Production Build

We build the production system with proper RAG pipelines, agentic workflows, HIL review, and observability.

Deliverables:

  • ✓Production-grade system
  • ✓Documentation
  • ✓Deployment pipeline
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Step 3 of 5
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Step 4 of 5
4

Launch & Monitor

We deploy to production, set up monitoring, and establish feedback loops for continuous improvement.

Deliverables:

  • ✓Live system
  • ✓Monitoring dashboards
  • ✓Runbooks
5

Iterate & Improve

Based on real usage data and feedback, we continuously optimize retrieval, re-ranking, and agent performance.

Deliverables:

  • ✓Performance reports
  • ✓Optimization recommendations
  • ✓Knowledge transfer
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Step 5 of 5

Technical Architecture

Our RAG + Agents + HIL architecture

Data SourcesIngestChunkEmbedVector DBRe-rankTools/AgentsHIL ReviewShipRetrieval PipelineHuman-in-the-Loop

RAG Pipeline

Connectors, chunking strategies, embedding models, vector stores, re-ranking algorithms

Agentic Workflows

Tool-use, retries, guardrails, observability, multi-agent orchestration

Human-in-the-Loop

Review queues, escalation rules, sampling rates, feedback loops