AI & Intelligent Systems
We build AI into production software: document parsing, prediction models, retrieval systems, and agents that complete real tasks. Every system is measured against a baseline, because AI that cannot be evaluated cannot be trusted. We work with Claude and OpenAI models, and self-hosted models where data residency requires it.
Most AI projects fail in the same place. The demo works, the pilot looks promising, and then nobody can say whether the system is right often enough to depend on. Without an evaluation set, there is no way to answer that question, and no way to tell whether a change made things better or worse.
We build the evaluation first. Then the system. That order is the difference between AI that ships and AI that stays in a pilot.
What this covers
Document and data extraction
Turning unstructured documents into structured records. Our own FleetChart product parses freight rate confirmations this way, extracting rates, lanes, dates and broker details into fields an operator can act on.
Prediction and pattern analysis
Models that learn from your historical data. Nigraan analyzes property transaction patterns to predict pricing for real estate agents who previously estimated it by hand.
Retrieval systems
Search over your own documents and data that returns answers with citations, so users can verify what the system told them.
Agents that complete tasks
Systems that take an instruction and carry it through a real workflow, with defined boundaries, logging, and a person in the loop where the cost of being wrong is high.
Evaluation and monitoring
Test sets, accuracy baselines and regression checks. You get numbers on how the system performs, and an alert when performance drifts.
How we run it
How a AI & Intelligent Systems engagement works
- 01
Define correct
Before any model work, we agree what a right answer looks like and assemble examples. This becomes the evaluation set everything is measured against.
- 02
Baseline
We measure the simplest approach that could work. Sometimes it is good enough, and knowing that saves you a great deal of money.
- 03
Build and measure
Each change is scored against the evaluation set. Improvements are demonstrated rather than asserted.
- 04
Put a person in the loop
Where being wrong is costly, the system routes to a human instead of guessing. Confidence thresholds are set with you, not by us.
- 05
Monitor
Accuracy, latency and cost are tracked in production. Model providers change behavior, and you should find out from a dashboard rather than a customer.
Evidence
Where we have done this
FAQ
Questions about AI & Intelligent Systems
- How do you know the AI is accurate enough?
- We build an evaluation set from real examples before writing the system, then score every version against it. You see accuracy as a number, measured on your own data, rather than as an impression from a demo.
- Which models do you use?
- Primarily Claude and OpenAI models, chosen per task rather than as a standard. Where data residency or cost requires it, we run open models on infrastructure you control. We keep the model layer swappable so you are not locked to one provider.
- What happens to our data?
- It stays in your accounts. We use provider APIs configured to exclude your data from training, and where a client requires data never to leave their infrastructure, we deploy self-hosted models instead.
- Is AI the right answer for our problem?
- Often it is not, and we will tell you. Plenty of problems described as AI problems are better solved with rules, a database query, or fixing the process upstream. We would rather say that during scoping than six weeks in.
- What does it cost to run?
- Model inference is a per-use cost, so it scales with volume rather than sitting as fixed overhead. We instrument cost per request from the start and design around it, including caching and routing simpler requests to smaller models.
Related services
Available to clients in the US, the UK, Canada, United Arab Emirates, Saudi Arabia and New Zealand. See all locations
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