For firms and agencies building their own
AI & Automation Consulting
You don't want the analysis done for you. You want the pipeline, and you want to own it.
The problem
Plenty of organisations have the same document problem every month. At that frequency, outsourcing each round stops making sense — what you want is the capability sitting inside your own walls.
The usual route is a vendor platform, and the usual result is a subscription, a data model you did not choose, a governance layer that forbids the one thing you needed, and an export path that is somehow always worse than the import path.
The alternative is a system assembled from open components, fitted to how your organisation actually works, documented, and handed over. You run it. You can read it. You can change it without asking anyone.
What you get
- Document ingestion and OCR pipelines tuned to your document types and their real condition
- Searchable, cited retrieval over your own corpus — answers that resolve back to a source page
- Accuracy harnesses: controlled ground-truth sets so you can measure the system, not trust it
- Integration with the systems already in place rather than a parallel stack beside them
- Internal tooling and workflow automation for the processes quietly consuming staff hours
- Documentation, handover and training so the system outlives the engagement
How the work runs
Find the real bottleneck
Usually not where the request says it is. A short discovery pass looks at the actual process before anything gets built.
Prototype against real documents
Built on your material, not a demo set, so the hard cases show up in week one instead of month six.
Measure before scaling
A ground-truth set and an accuracy harness come before production. Systems that cannot be measured cannot be trusted.
Hand over the keys
Source, documentation, and a working knowledge transfer. Continued involvement should be a choice, not a lock-in.
Common questions
What does the stack look like?
Open components, chosen per project: standard OCR and document-layout tooling, general-purpose language models accessed through their APIs, ordinary databases and search infrastructure. Nothing proprietary to this practice that you would need a licence to keep running.
Why not just use a commercial platform?
Sometimes you should, and I will say so. A platform wins when your problem is genuinely standard and the vendor's assumptions match yours. Custom wins when the shape of your documents or your workflow is unusual, when the data cannot leave your environment, or when the platform's governance layer blocks the specific thing you need.
Can you work alongside our existing developers?
That is often the best arrangement. Your team knows the domain and the existing systems; this engagement supplies the document-processing and evaluation piece, then transfers it.
How do you keep a language model from making things up?
Structurally, not hopefully. Answers are constrained to cited spans from source documents, results are re-derived independently and disagreements surfaced, and the whole system is scored against a held-out ground-truth set so drift is visible. Anything that cannot be grounded in a source is reported as unsupported.
What size engagement makes sense?
From a two-week proof of concept through a multi-month build. A discovery conversation and a small paid prototype are the usual starting point, and the prototype is designed to be a real answer about feasibility rather than a demo.
Start with a sample of your corpus
Send a representative slice — a few hundred pages is plenty. You get back the processed output, an accuracy report against that slice, and a fixed price for the full job before any commitment.