Summarization
Long conversations, project comment threads, support histories and meeting notes condensed into the few lines somebody actually needs before they act.
Practical AI inside software you already run
We look for the places in an existing workflow where AI removes real effort, and where the result can be trusted by the people relying on it.
Our position
Start from the workflow, not the technology
The useful question is not "where can we put AI?" It is "which parts of this job are people doing by hand because the software cannot help them yet?"
That is usually reading long threads to find one decision, re-keying information that already exists somewhere else, hunting through documents, or writing the same summary every week. Those are the places worth automating.
Because we also build and maintain the underlying business systems, we can connect AI to your real data, your real permissions model and your real workflows — rather than bolting on a chatbot that has no idea what your business does.
Where it earns its place
Capabilities we add to existing applications
Information extraction
Pulling structured values out of unstructured business data — documents, emails, free-text fields — and writing them back into the system as real records.
Search across your own data
RAG-based retrieval over your projects, documents and history, so people can ask a question in plain language instead of knowing which screen to open.
Internal knowledge assistants
An assistant that knows your processes, policies and terminology — useful for onboarding, support teams and anyone who keeps asking the same colleague.
Project-specific assistants
Scoped to one project, job or customer, with access to that record's history, documents and status — answering questions without exposing everything else.
AI-assisted reporting
Narrative explanations alongside the numbers: what changed, what looks unusual, and which items need attention this week.
Private and local LLM deployment
For many businesses the blocker is not capability, it is confidentiality. Customer records, financial data, contracts and internal communications often cannot be sent to a third-party service.
We have worked with private and local LLM deployments that run inside your own infrastructure, so sensitive information never leaves the environment you control. The technology choice follows the requirement — local platforms, hosted APIs, or a mix, depending on what the data policy allows.
How we approach it
Careful, scoped and measurable
- Pick one workflow. We start with a single task where the time saving is obvious and easy to verify.
- Ground it in your data. Retrieval over your own records, respecting the same permissions the rest of the application enforces.
- Keep a human in the loop. Output is presented for review where the consequences of being wrong are material.
- Measure the result. If it does not save time or improve accuracy, we say so rather than expanding it.
- Then extend. Once one workflow proves out, the same foundation supports the next.
The honest version
Some workflows are not worth automating, and some data is not clean enough to build on yet. We would rather tell you that early than deliver a feature nobody trusts. Where AI does fit, it tends to fit very well — and the groundwork that makes it possible (clean APIs, structured data, sensible permissions) improves the application either way.
Let's work together
Where would AI actually help you?
Tell us which manual task your team repeats most often. That is usually the right place to start, and it is a short conversation to find out.