Services · AI Integration

AI that earns its place in production.

Most AI projects fail because they start with a model instead of a problem. We start with the hours your team loses every week — then build the smallest AI feature that wins them back.

What we build

Working AI, not demos.

Document assistants

Ask questions across your PDFs, policies, tickets, and wikis — with answers grounded in your actual documents.

LLM product features

Summarization, drafting, classification, and extraction built into the tools your team already uses.

Workflow automation

Intake triage, data entry, and routing handled automatically — with humans in the loop where it matters.

Data pipelines

The unglamorous plumbing that makes AI reliable: clean ingestion, indexing, and retrieval over your data.

Evaluation & guardrails

Test suites for AI behavior, cost and quality monitoring, and guardrails against off-script output.

AI strategy & audits

An honest map of where AI would pay off in your business — and where it's cheaper to write a script.

How it goes

Small bets first.

  1. 01

    Find the leverage

    A short discovery to locate the repetitive, language-heavy work where AI actually moves the needle.

  2. 02

    Prove it on your data

    A focused pilot against your real documents and edge cases — not a polished demo on cherry-picked examples.

  3. 03

    Ship with guardrails

    Production rollout with evaluation, monitoring, and fallbacks — so quality doesn't drift after launch.

  4. 04

    Expand what works

    Once one workflow pays off, we extend to the next — compounding wins instead of betting everything at once.

Our stance

Practical AI, honestly sold.

We're enthusiastic about what modern models can do and blunt about what they can't. Some processes need a fine-tuned pipeline; many just need a well-prompted API call; a few need no AI at all.

You'll get that assessment straight — before you've committed a budget to any of it.

FAQ

Common questions.

Which AI models do you use?

We're provider-agnostic — Claude, OpenAI, or open-source models, chosen per task for quality, cost, and data-handling requirements. The architecture keeps you free to switch as models improve.

What happens to our data?

Your data stays yours. We design for least exposure — API providers with no-training agreements, private deployments where needed, and clear documentation of exactly what leaves your systems.

How do you keep AI features from going off the rails?

Evaluation suites run AI behavior against real examples before and after every change, outputs are grounded in your data with citations where possible, and human review is built in for high-stakes actions.

What does an AI project cost to run?

Usually less than people expect — most workflows run on efficient models for cents per task. We estimate usage costs up front and build in monitoring so there are no surprise bills.

Will you tell us if AI is the wrong tool?

Yes, and we regularly do. If a rules-based script or a database query solves the problem, that's what we'll recommend — it's cheaper for you and better for the relationship.

Next step

Wondering where AI fits your business?

Tell us about the work that eats your team's week. We'll tell you honestly whether AI can win it back — and what that would take.

Start the conversation

or call 405-385-9082