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From demo to a process that bills

Applied artificial intelligence

Agents, copilots and semantic search wired into your systems — with measurement, spend limits and human control where it counts.

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You've already run the AI experiment. The problem now is a different one: how to connect a model to your data without leaking it, how to know whether it answers well, how to keep the bill from running away, and how to fit it into a process that already operates. We build that part — the part that turns an impressive demo into a system your operation depends on.

What we build

  • Agents that do the work

    Not just answers: they quote, triage tickets, reconcile invoices, fill forms and trigger actions in your ERP or CRM through tools and MCP. With human approval on the steps that need it.

  • RAG over your knowledge

    Contracts, manuals, policies, case files and email turned into answers that cite their source. Incremental ingestion, per-user permissions and reranking so the right answer isn't buried at position four.

  • Internal copilots

    Assistants per function — sales, support, finance, legal — living where your people already work: the CRM, Slack, the internal portal or the ERP.

  • Documents and vision

    Structured extraction from PDFs, invoices and records; OCR and image inspection when the data arrives on paper or in a photo.

  • Evaluation and observability

    Eval suites built from real cases, per-request traces, and dashboards for quality, latency and cost per token. What isn't measured doesn't ship.

  • Governance and security

    Content guardrails, PII redaction, per-tenant isolation, access logs, and models running inside your own AWS account through Bedrock.

What you end up with

  • One concrete process running on AI, not a demo
  • Quality and cost metrics you can review weekly
  • The code and the prompts in your repositories
  • Your team able to extend it without us

What we use

  • Claude
  • OpenAI
  • Amazon Bedrock
  • LangGraph
  • MCP
  • pgvector
  • OpenSearch
  • Python

FAQ

Will my data train someone else's model?

No. We use models through enterprise APIs or inside your own AWS account with Bedrock, where the provider doesn't train on your content. If the case demands it, we run open models on your own infrastructure.

What does this cost to run per month?

It depends on volume, but we model it before building and instrument it afterwards: you'll see cost per conversation, per document or per transaction, with alerts and caps.

How long until the first result?

A scoped use case usually reaches a usable pilot in four to six weeks, and production in eight to twelve.

Tell us what you want to build

Thirty minutes, no slide deck. We leave the call with a clear read on scope and on whether we're the right team.

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