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AI Agents & Assistants

This is the practice behind our flagship deployment: an AI agent answering from thousands of internal documents, used daily by 150+ people, that cut data-operations workload tenfold. We build agents the same way for SMBs — grounded in your data, measured before they’re trusted, monitored after they ship.

What You Get

Agents that answer from your data — and can prove it.

Grounded, cited answers

Every response is retrieved from your documents and data, with the source attached. When the evidence isn’t there, the agent says so and routes to a person.

Privacy engineered in

PII detection and redaction before anything reaches a model, access controls that mirror your existing permissions, and an audit trail on every answer.

Measured before trusted

An evaluation suite runs before launch and on every change: real questions from your team, scored for accuracy. Quality is a number, not a vibe.

Costs under control

Caching, context management, and model routing designed in from the start — the disciplines that cut one production system’s token spend by 90%.

Scope

What’s included.

  • Use-case & data design — what the agent answers, from which sources, for whom
  • Retrieval architecture — ingestion, chunking, and search tuned to your documents
  • Agent build — single or multi-agent design, with human handoff paths
  • PII & access controls — sanitization, permission-aware retrieval, audit logging
  • Evaluation suite — golden questions, accuracy scoring, regression checks
  • Production deployment — in your cloud, with monitoring and a handover walkthrough

At a Glance

  • Method: Proof-First Build — fit call, free Blueprint, pilot, production
  • Pilot: 30–60 days on a bounded slice of your operation
  • Flagship result: 150+ daily users, 10x data-ops productivity
  • Ownership: Your accounts, your code, your IP
  • After launch: AI Operations (LLMOps) available
FAQ

Questions about this service.

How is this different from turning on a chatbot product?
Products give you a model and a hope. This is engineering: your data prepared and permissioned, retrieval tuned to your documents, accuracy evaluated against your team’s real questions, and cost and quality monitored in production. That’s the difference between a demo and a system 150 people rely on.
What data can the agent use?
Documents, wikis, tickets, databases, and internal systems — whatever your team currently searches by hand. The Blueprint reviews what’s usable and what needs preparation first.
What happens when the agent doesn’t know?
It says so, cites what it did find, and routes the question to the right person with context attached. Confident wrong answers are the failure mode we engineer against first.
Which models do you use?
The ones your constraints point to — cost, privacy, and accuracy decide, and the architecture keeps models swappable as the market moves. Model choice is a Blueprint decision, made with evidence.
Related

Often paired with.

Knowledge Assistants

The document-focused form of this practice.

Explore

AI Operations (LLMOps)

Keeping the agent accurate and affordable after launch.

Explore

AI Readiness Assessment

Where most agent projects properly begin.

Explore

Start with a fit call.

Thirty minutes. Bring the problem as it actually is — the stalled pilot, the bill, the spreadsheet everyone quietly maintains. We’ll tell you honestly whether we’re the right builder for it.

You leave with a straight answer on fit — and if it fits, a free Blueprint to follow.

Request a fit call How Proof-First Build works