Document intake and extraction
OCR, classification and field extraction for invoices, forms, contracts and PDFs that arrive in formats nobody agreed on.
Service
AIAutomation
Turn repetitive operational work into monitored pipelines.
Running
- Document AI
- Workflow orchestration
- Human-in-the-loop
- Integrations
Theproblem
Manual process work scales linearlywith headcount
Most operational cost is a person reading something, making one decision, and typing the result somewhere else. It never appears on a roadmap, it grows with volume, and it is where quiet errors come from.
Mustafa Tariq
AI Engineer · leads this service
Our approach
We map the processes your team runs by hand, then replace the rule-shaped and model-shaped steps with automated pipelines that keep an audit trail, and leave a human in the loop wherever the cost of being wrong is high.
What youget
- Process map of the workflow as it actually runs today
- Production pipeline with monitoring and alerting
- Admin console for reviewing runs and exceptions
- Runbook, escalation path and handover session
- Manual touchpoints
- Reduced
- Audit trail
- Complete
- Exception handling
- Explicit
Capabilities
What AI Automationincludes
Scoped per engagement. We start with whichever of these removes the biggest constraint first.
Workflow orchestration
Queues, retries, idempotency and scheduling across systems that were never designed to talk to each other.
Human-in-the-loop approvals
Explicit review gates with confidence thresholds, so the model handles the routine and a person handles the exceptions.
Monitoring and audit trail
Every run is inspectable after the fact, failures alert a person, and each decision records why it was made.
System integrations
CRM, ERP, ticketing, storage and internal APIs wired in through documented, versioned connectors.
Rollout without a big bang
The pipeline shadows the manual process first. Disagreements get reviewed before it takes over anything.
Delivery
Howa project runs
Typical shape for this service. Timings move with scope, the order does not.
- 01
Observe
Week 1
We sit with the people doing the work and record the process that exists, including the exceptions they handle without noticing.
- 02
Design
Week 1–2
We split the steps into rule-shaped, model-shaped and judgment-shaped, and agree what stays human.
- 03
Build & shadow
Week 2–6
The pipeline runs alongside the manual process and its disagreements are reviewed before cutover.
- 04
Operate & hand over
Ongoing
Alerting, runbook and the escalation path for the day it does something unexpected.
Typicalstack
Tools we reach for
Chosen per engagement and biased toward what your team can maintain after we leave.
- Python
- Temporal
- PostgreSQL
- Redis
- AWS
- Docker
Commonquestions
The questions we getabout Automation
Do we need clean data before we start?
No. Most of what we automate begins with messy input, which is usually the reason the work is manual in the first place. Cleaning is part of the pipeline, not a prerequisite.
What happens when the model is unsure?
It routes to a person. Every automation we ship has a defined confidence threshold and a review queue behind it.
Can this run on our infrastructure?
Yes. We deploy to your cloud account or on-premise if data residency requires it.
Starthere
Tell us the process,not the solution.
The most useful first message describes what someone on your team does by hand today and how often. That is enough for us to tell you whether it is worth building.
What happens next
- A named engineer reads it, not a form inbox
- Reply within 24 hours, even if we're not the right fit
- A 30-minute call to trace the process end to end
- A fixed-scope quote, or an honest no