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SERVICES · APPLIED AI

Ship AI into production. Keep the evals.

Embedded senior engineers who take one use case from idea to a running system inside your environment — retrieval, agents, fine-tuned or small models — with an evaluation suite that catches regressions before your users do.

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What done looks like.

  • Grounded answers with citations and permissions honoured
  • Agents with human approval gates and audit trails
  • A versioned prompt library with regression tests
  • A model gateway so you can swap providers
  • An eval suite in your CI
  • Hand-over documentation your team owns

Agentic AI in production.

Agents that each own one job. Each agent does its job end to end, hands off to the next, and leaves the decision with a person: skill weights, shortlists and every outbound message are approved before anything moves. Figures marked projected come from the delivered design.

  • Requisition Agent: turns each new SOW into a standard, weighted JD and opens the requisition in the HRMS, linked to the CRM deal (27% fewer manual steps, projected)
  • Resource Match Agent: ranks internal, bench and past candidates by fit and cost; the resourcing head reviews every shortlist (12 days faster requisition-to-shortlist, projected)
  • Interview Orchestration Agent: triggers interview requests, syncs scorecards to the HRMS and tracks interview-partner SLAs (4 days faster first-interview cycle, projected)
  • HR Self-service Agent: answers employee HR queries around the clock, integrated with Workday, CRM, the knowledge base and third-party insurance vendor systems, plus a mobile app, for a firm of 12,000+ employees (40% less HR ticket volume)
  • Assessment Agent: generates randomised question sets and scores candidate assessments automatically for campus recruitment

Stack.

Models
OpenAI · Anthropic · Llama · Mistral · Qwen · Gemma
Retrieval
LlamaIndex · LangChain · LangGraph · pgvector · Qdrant
Documents
Azure AI Document Intelligence · AWS Textract · Unstructured
Evals
Ragas · Promptfoo · Langfuse · Braintrust
PROOF

We build our own product this way.

  • Cited chat with provenance and epistemic markers
  • Trajectory narration with template fallback
  • Config agent that parses intent into validated plans
This is how our own product answers questionsDesigning RAG systems that survive production

Talk to an engineer.

Scope and investment range within one business day.

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What Palantir and OpenAI call forward deployed engineering — at mid-market price points.