AI Engineering
LLM systems that survive contact with production — retrieval, tool use, evaluation, and the logging that lets you explain a decision afterwards.
SOLUTION · AUTOMATION
AI Automation
The repetitive judgment work handled, with a record of why each decision was made.
TARGET PROFILE
Who this architecture is engineered for.
Built specifically for operational teams experiencing structural friction at scale.
The profile
For a team where several people spend a large part of the week reading documents, emails, or tickets and making the same category of small decision — routing, classifying, extracting, summarising — and where the decision itself is not hard, but there are several hundred of them a day.
The core bottleneck
There is a category of work that resists conventional automation because it requires reading something unstructured and deciding what it is. Rules handle the clean cases and fail on everything else, so the rules grow, and eventually maintaining them costs more than the work they replaced. A language model handles the messy cases well, which is why this is now tractable — but it also produces a confident answer when it should produce none, and that is the risk that stops most of these projects. What makes the difference is not the model. It is treating uncertainty as a first-class output: knowing when the system should decide, when it should ask, and recording enough that anybody can reconstruct what happened on a specific item three months later.
SYSTEM MODULES
What we build and integrate.
Every component is engineered for multi-tenant security, auditability, and zero-downtime maintenance.
A task definition specific enough to be graded, with real examples of correct and incorrect handling
Extraction and classification against your documents, measured on a held-out set rather than demonstrated
Confidence thresholds that route uncertain items to a person instead of guessing
A review interface where correcting the system is faster than doing the task by hand
Corrections fed back as new evaluation cases, so the system's accuracy is tracked rather than assumed
Full decision traces: the input, what was retrieved, what the model returned, and what happened next
Cost and throughput reporting per completed item, not per model call
ARCHITECTURE
System composition & topology.
How the core modules, data boundaries, and integrations connect in production.
Node 01
Document and message sources
Mailboxes, file stores, ticket systems, or an inbound interface — with the original preserved unchanged so a decision can always be traced back to what was actually received.
Connects to
- extraction
Node 02
Extraction and classification
Turning unstructured input into typed fields and categories, validated against a schema so a malformed response is a handled case rather than a downstream surprise.
Connects to
- confidence
- knowledge
Node 03
Reference knowledge
The policies, price lists, product data, or precedents the decision depends on, retrieved per item so the system is working from current information rather than what it was trained on.
Connects to
- extraction
Node 04
Confidence routing
The threshold that decides whether an item is handled automatically or sent to a person. Set from measured accuracy per category, and adjustable as the numbers change.
Connects to
- actions
- review
Node 05
Human review queue
Where uncertain items go, presented so that confirming or correcting takes seconds. Every correction becomes a new evaluation case.
Connects to
- actions
- evaluation
Node 06
Downstream actions
Whatever the decision triggers — a ticket routed, a record updated, a reply drafted — with a defined behaviour for the case where the action itself fails.
Connects to
- trace
Node 07
Evaluation set
Graded cases run on every change to prompts, models, or retrieval, so an improvement is a measurement rather than an impression.
Connects to
- confidence
- trace
Node 08
Decision trace
Per item: input, retrieved context, model response, confidence, route taken, and outcome. This is what answers the question about a specific case months later.
Connects to
- evaluation
TECHNOLOGY DEPTH
Technologies powering AI Automation.
Standardized architecture frameworks and data components integrated in this solution.
Frontend
- TypeScript
Backend
- Python
- Node.js
Data
- Vector search
- PostgreSQL
- Redis
Cloud
- Docker
AI
- OpenAI
- Anthropic
QUESTIONS & ANSWERS
Questions regarding ai automation.
Direct answers to common technical and engagement questions.
It varies enough by task that any number quoted before measurement is guesswork. The honest answer is that we find out during a scoped evaluation stage, on your data, and report the accuracy by category. That result then decides where the confidence threshold sits — and it sometimes decides that only part of the task is worth automating.
Two mechanisms. Items the system is unsure about never get decided automatically — they go to a person. And every automated decision is recorded with its full context, so an error found later can be traced, corrected, and added to the evaluation set. Neither removes errors; together they mean errors are bounded and visible rather than silent.
Not if the review queue is designed properly. The people who did the task become the people who handle the difficult cases and correct the system, which is where their judgment was always most valuable. The routine cases were never the part that used their expertise.
Rules are exact and brittle: they handle the cases anticipated when they were written and fail on the rest, which is why rule sets grow until they are unmaintainable. A model generalises to cases nobody wrote a rule for, and reports uncertainty when it should. In practice the two work together, with rules covering what is genuinely deterministic.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.