AI integration and RAG inside the software you already run

The model is the easy part; connecting it to your data without leaking it is the work

A prototype that answers questions about a PDF takes an afternoon. The hard part comes after: hundreds of documents with contradictory versions, different permissions per user, an ERP reachable only through an old API, and nobody who can say how often the answer is right. We integrate AI into your software instead of building yet another standalone tool: ingestion and chunking of your documents, retrieval that respects who can see what, tools exposed to the model through MCP with scoped credentials, and an eval set that decides when it is ready. If your use case is a private assistant for your team, we deploy it in your cloud so the data stays inside your environment.

• 01Map of data sources, permissions and the data flow to the model, agreed before we build
• 02Ingestion, chunking and indexing pipeline over your documents, with incremental updates
• 03Retrieval with hybrid search, source citations and per-user permission filters
• 04Integration with your existing software through its API, or a custom MCP server that exposes your tools to the model
• 05Eval set built from real questions your team asks, used as the acceptance criteria
• 06Audit log, cost-per-query controls and a runbook for your team
• 07Optional: a private AI assistant deployed in your cloud account

• Assistant over policies and manuals

  Your team searches shared drives and wikis for answers that live in long documents. The assistant retrieves the relevant passages, answers with citations, and only draws on documents the person asking is already allowed to see.

• Model features inside an existing product

  Your product has a form, a ticket view or a report that would be better with a model behind it. We add the feature through your existing API, with logging, cost limits and an eval set that catches regressions when the model or prompt changes.

• Tools exposed to a model through MCP

  Your systems already hold the data and actions an assistant would need. We build an MCP server that exposes a narrow set of tools with scoped credentials, so the model can look things up or act without direct access to the underlying systems.

1. 01

   Data map and use cases

   We identify which documents and systems feed the use case, who has access to what, and which data must never reach a model. That produces the scope of the first release and a data flow your security team can review.

2. 02

   Evals and baseline

   Before building, we assemble with your team a set of real questions and their correct answers. We measure a simple baseline so every later design decision is compared against numbers, not impressions.

3. 03

   Build and integrate

   We build the ingestion, retrieval and tool layer and connect it to your software through its API or MCP. Each tool uses scoped credentials, and every write action is logged.

4. 04

   Launch and handover

   We roll out to a pilot group, tune against real failures and hand code, runbook and evals to your team. If you want us to operate it, AgentOps covers what comes after.

• AI in internal processes: guide for mid-market companiesWhere AI has clear ROI, where it is marketing, and how to avoid paying OpenAI to do what a bash script does better.
• Build vs buy AI: a decision matrix for 2026When to buy an AI product, when to build, and why most companies end up with a hybrid. Includes a decision matrix and a two-week process.
• How much does it cost to build an AI agent in 2026What an AI agent costs to build and to run: published agency estimates, our starting prices, operating costs and the factors that move a quote.