Services / Intelligence · CAP.008
AI systems built for production, not for the demo.
Document processing, support automation, retrieval systems, and agents, engineered with the evaluation, guardrails, and human-override paths that separate production AI from an impressive prototype.
01 · When you need this
If any of these sound familiar, you’re in the right place.
- A team keys data out of PDFs, emails, or invoices all day, every day.
- Support volume grows faster than the support team can.
- Your organization's knowledge lives in a thousand documents nobody can search.
- You built an AI prototype that wowed everyone and now doesn't survive real inputs.
- The AI strategy report (ours or anyone's) says build, and now it needs building properly.
02 · What we deliver
Exactly what you walk away with.
No vague 'solutions'. These are the concrete deliverables: each one is a line in the proposal and a checkbox at handover.
DEL.01
The system
Extraction, automation, retrieval, or agent, integrated into your actual tools, not a separate tab.
DEL.02
Evaluation harness
A scored test set from your real data. Accuracy is measured, not vibed, before and after every change.
DEL.03
Guardrails & fallbacks
Confidence thresholds, human review queues, and a defined behavior for 'the model isn't sure.'
DEL.04
Monitoring dashboard
Accuracy, cost, and volume over time: drift gets caught by you, not by a customer.
DEL.05
Runbook & handover
How to retrain, adjust thresholds, and swap models. You're not married to us or to one vendor.
03 · How it runs
Phase by phase, with nothing hidden.
Every phase states what happens and what you see in your project portal while it does.
Data & baseline
1-2 weeks
Build the evaluation set from your real cases and measure the honest baseline.
In your portal: The eval set and baseline scores, the numbers everything is judged against.
Build
2-6 weeks
The pipeline, integrations, and review surfaces, with eval scores published weekly.
In your portal: Weekly accuracy/cost numbers, good or bad.
Shadow run
1-2 weeks
The system runs alongside your humans on live work; disagreements become training data.
In your portal: Shadow-run scoreboard: human vs system, case by case.
Graduated rollout
1-2 weeks
High-confidence cases go automatic; everything else routes to humans. The threshold moves only as evidence allows.
In your portal: Rollout dial and live monitoring.
04 · Standards
Non-negotiables, in writing.
Eval before build
No pipeline work starts until the test set exists. It's the contract between us and reality.
A human path, always
Every automated decision has a review queue and an override. No dead ends for your customers.
Cost ceilings
Per-task model spend is capped and monitored, no surprise five-figure API bills.
Model-swappable
Built against abstractions so next year's better/cheaper model is a config change, not a rebuild.
05 · Stack
Tools chosen for your handover, not our comfort.
The model is a component, not the architecture. The durable value is the evaluation set, the data pipeline, and the integration; those outlive any model generation.
06 · Questions
Asked often, answered straight.
What accuracy can we expect?
What about hallucinations?
Does our data train someone else's model?
Ready to talk about custom ai systems?
Describe where you are. We’ll respond within one business day with honest next steps, even if the honest next step isn’t us.
Start the conversation