AI & automation

AI where it actually saves hours.

Not a chatbot bolted onto your homepage. AI applied to the repetitive work that quietly eats your team's week — and measured by the hours it gives back.

Good candidates

The work AI is genuinely good at.

If your problem isn't on this list, we'll tell you — plenty of things are better solved with plain software.

What we build

Five things that pay for themselves fast.

Each one starts small, proves the saving, then expands.

01

Document processing

Invoices, contracts, receipts and forms read automatically and pushed into your system — with a human check where it matters.

02

Internal search & assistants

An assistant that answers from your own documents and data, with sources — so nobody has to trust it blindly.

03

Support automation

First-line answers handled automatically, with clean escalation to a person when confidence is low.

04

Classification & routing

Incoming leads, tickets and requests read, tagged and sent to the right place without a human triaging them.

05

Workflow automation

The chain of copy-paste between systems, replaced with something that runs on its own and tells you when it fails.

06

AI inside your product

Features your customers see — summaries, drafting, semantic search — built into the software you already sell.

Tools

Model-agnostic, on purpose.

We pick per task and keep the swap cheap — this field moves too fast to marry one vendor.

ClaudeOpenAIGemini Open-source LLMs RAGVector databases PythonLangChain n8nZapierMake AWS Bedrock
FAQ

The questions we get asked first.

How much does an AI automation cost?

A focused automation — one document type, one workflow — starts at $3,000. A full internal assistant over your company's knowledge usually runs $15,000 to $40,000. We always start with the piece that pays for itself fastest.

Will our data be used to train someone's model?

No. We use enterprise API tiers where data isn't retained for training, and for sensitive material we can run open-source models inside your own infrastructure. This is decided before anything is built.

What if the AI gets something wrong?

We design for it. Every automation has a confidence threshold, a human review step where the cost of a mistake is high, and a log of what it did — so errors are visible and correctable, not silent.

Is AI actually the right answer for us?

Sometimes not — and we'll say so. Plenty of problems marketed as AI problems are solved better, cheaper and more reliably with ordinary software. We look at the process first and recommend the boring solution when it wins.

How do we know it's working?

We agree on the metric before starting — hours saved, tickets deflected, processing time per document — and instrument it. You get numbers, not impressions.

Other services

We also build

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