Document intelligence
Extract structured data from invoices, contracts, and regulatory filings. Our models handle scanned PDFs, handwritten annotations, and inconsistent layouts. One logistics client cut manual data entry by 74% within three months.
How a project moves from idea to production
Most AI projects fail because the model never leaves a notebook. Ours ship. Here is the sequence we follow, and roughly how long each step takes.
1
Data audit (week 1)
We look at what you actually have: databases, spreadsheets, PDFs, API logs. We check volume, quality, and whether the labels are trustworthy. If the data is messy, we say so early rather than pretending a model will fix it.
2
Proof of concept (weeks 2–4)
A small model trained on a subset of your data, evaluated against a metric you care about. Accuracy alone is rarely enough; we usually track precision, recall, and the cost of each type of error in pounds.
3
Pipeline engineering (weeks 4–7)
The model gets wrapped in an API, connected to your source systems, and tested under realistic load. We write monitoring hooks so you can see when predictions drift.
4
Deployment and handover (week 8)
We deploy to your infrastructure or a managed cloud account that you own. Documentation covers retraining steps, rollback procedures, and who to call at 2 a.m. if something breaks.
What we build
Each of these is a real capability we have delivered, not a menu item we hope someone will order.
Predictive maintenance
Sensor data from motors, pumps, or HVAC units feeds a time-series model that flags failures 5–14 days before they happen. We integrate with your existing SCADA or IoT gateway; no new hardware required.
Customer intent classification
Emails, chat transcripts, and support tickets get labelled by intent in real time. Route warranty claims to one queue, billing disputes to another, upsell signals to the sales team. Typical accuracy: 91–94% after two weeks of fine-tuning on your historical tickets.
Computer vision for quality control
Cameras on your production line feed images to a defect-detection model trained on your specific product. We have deployed this for food packaging, PCB assembly, and textile weaving. False-positive rates sit below 2% after calibration.
Data pipeline design
Before any model can work, data needs to arrive clean, on time, and in the right shape. We build ETL pipelines with Airflow, dbt, or Prefect, depending on your stack, and set up alerting for schema changes or missing batches.
Questions we hear often
If yours isn't here, the contact form is right below.
It depends on the task. For document extraction, a few hundred labelled examples are usually enough to get a useful proof of concept. Classification tasks like intent detection need more, typically 2,000–5,000 labelled records, though we can use transfer learning to reduce that. During the data audit we give you an honest assessment. If there isn't enough, we help you design a labelling workflow so you can collect what's needed in weeks rather than months.
We deploy on AWS, Azure, and GCP. If you run on-premise servers, we can work with those too, provided they meet minimum GPU or CPU specs. We never lock you into a platform you don't already use.
You get full source code, documentation, and a retraining playbook. We offer optional support contracts billed monthly, covering model monitoring, drift detection, and retraining runs. Many clients manage retraining themselves after a few cycles; we are fine with that.
No. Anyone who guarantees accuracy before seeing your data is guessing. What we do guarantee is a clear evaluation at the proof-of-concept stage with agreed metrics. If the numbers don't meet the threshold you set, you can walk away. We have killed projects at that stage twice; both times the client thanked us for not wasting their budget.
We sign data-processing agreements before any data transfer. For healthcare and financial-services clients we have worked inside air-gapped environments where data never leaves the client's network. Our engineers hold SC clearance where required.
Talk to our engineers about your data
Describe what you're trying to automate. We'll reply within one working day with an honest take on whether AI is the right tool.
