AI Engineering & Integration
Put models where they do measurable work, and keep a human on the decisions that matter.
AI is most valuable in the boring middle of a process: reading a supplier invoice, pulling structured fields out of an unstructured document, classifying an inbound message, drafting the first version of something a person will edit.
That kind of work needs engineering, not prompting. It needs a review step where a person confirms before anything is written to the ledger. It needs key rotation and provider fallback so a rate limit does not stop the line. It needs to report honestly when it is not configured, rather than failing silently.
We build the pipeline around the model (extraction, validation, human confirmation, write-back) so the model is a component you can swap rather than the thing the whole system depends on.
What you end up with
Document capture that people trust
Photograph a supplier invoice, the model reads the line items, a person reviews and confirms, and only then do stock levels, cost prices and balances move.
Provider redundancy by default
Multiple keys per provider, rotated on a schedule and advanced automatically on rate limit, quota or auth failure, then across to the second provider before giving up.
Humans on the irreversible steps
Models draft, extract and suggest. People approve anything that writes to the books, sends to a customer, or cannot be undone.
Cost you can see
Model choice matched to the job (a small fast model for classification, a stronger one for hard extraction), with usage visible rather than a surprise at month end.
What actually lands
Shaped to the engagement, but this is the shelf it comes off.
- 01Extraction and classification pipelines with schema validation on the output
- 02Review-and-confirm interfaces so a person is in the loop before the write
- 03Multi-provider key rotation and failover across Anthropic and OpenAI
- 04Retrieval over your own documents and records where it genuinely beats a query
- 05Agent and tool-use integrations, including MCP servers over your internal systems
- 06Evaluation set and regression checks so a prompt or model change is measurable
Where this has shipped
AI integration, specifically
Accurate enough to be worth it, and never trusted blind. The design assumption is that extraction is a first draft: the model proposes line items, the person confirms or corrects them on screen, and the confirmation is what triggers the write. That turns a data-entry job into a review job, which is both faster and safer than either extreme.
Not on the standard commercial API terms of the major providers, which is what we build against. Where the data is sensitive enough that this still matters, the honest answer is to scope what is sent (extract the fields the model needs rather than posting whole records), and that decision is made explicitly during the build.
Whichever is cheapest for the job it can actually do. Classification and routing rarely need a frontier model; hard extraction from a bad scan does. Because the pipeline treats the model as a swappable component with an evaluation set behind it, this is a setting rather than a rewrite.
Page copy drafted with AI assistance. Skofi is accountable for it. Editorial policy
Where this is applied
Tell us about the process that keeps costing you.
A 15-minute call is usually enough to say whether this is an automation, an integration, or something that should be built, and whether we are the right people for it.
What happens on the call
- 15 minutes, no deck
- We map the process on the call
- A written scope, or an honest no