AI systems and automation
We put AI inside the systems a business already uses, so it reads the documents, finds the right record and removes the steps nobody should be doing by hand.
What this solves
Companies accumulate work that is repetitive but not simple: invoices and reports that arrive as PDFs, information spread across systems that do not connect, questions that only one person can answer because only they know where to look. AI is good at that work when it is wired into real data with clear boundaries. We build that wiring, and the parts around it that decide what happens when the model is wrong.
When a business needs it
- ·People retype data from documents into a system.
- ·Answers exist somewhere internally but nobody can find them quickly.
- ·Two or three tools hold the same information and drift apart.
- ·A support or intake process is bottlenecked by manual review.
- ·An AI prototype works in a demo but not against production data.
What is included
Document processing
Intake, OCR, field extraction, validation rules and a review step for anything uncertain.
Retrieval
Search over your own documents and records, with permissions respected and sources shown.
Assistants
Interfaces that can read and write in your system through defined, audited actions.
Automation
Background jobs and event flows that replace manual copying between steps.
Integrations
Connections to ERP, CRM, storage, mail and whatever else already holds the data.
Evaluation
Test sets, accuracy measurement and monitoring, so quality is a number and not a feeling.
How we approach it
We start with one workflow and the real files behind it, not a general strategy. That gives an honest accuracy number in the first weeks.
The model is one component. Most of the work is the data path around it: what it can see, what it is allowed to do, what happens when confidence is low and who reviews the exceptions.
Everything runs in your own accounts and keys, so you can see what it costs as volume grows.
Technical depth
- Runtime
- Next.js, Node, Python workers
- Data
- PostgreSQL with pgvector, object storage
- Models
- Provider agnostic, swappable per task
- Queues
- Retries, dead letters, idempotent jobs
- Access
- Row level permissions, full audit log
- Hosting
- Your own cloud accounts
Questions we get
Do we need our own AI infrastructure?
No. In most cases we use hosted models through your own account, so you keep control of keys, data settings and cost without running anything yourself.
What happens when the model gets something wrong?
We design for it. Low confidence results go to a review queue, every output keeps its source, and accuracy is measured against a test set before anything runs unattended.
Can this work with our existing software?
Usually yes. If a system has an API we integrate with it. If it does not, we work through exports, files or the database, and we say early if something is not realistic.
How long does a first version take?
We scope the work around the system you actually need and agree the delivery plan before development starts. The first version usually covers one workflow end to end.