Document operations
Extraction, validation, review of the uncertain cases, routing, and an audit record of what was proposed and what a person approved.
AI that acts on your records, under your rules.
A chatbot bolted onto the side of a business answers a question and changes nothing. We put models inside the workflow instead — bounded by the same permissions as your people, and writing the result where the work already lives.
A practical delivery path
- 01Receive
- 02Interpret
- 03Validate
- 04Review
- 05Record
Use a model where the input is genuinely ambiguous. Anything a rule can decide stays a rule.
What is going wrong before this work starts.
- 01
An assistant beside the system changes nothing
It produces an answer and stops. Someone still has to open the record, enter the result, and remember to move the job to the next step.
- 02
Reading and re-keying consumes specialist time
Documents, emails, and forms are read, classified, summarized, and retyped before the real work can start, by the people hired for the real work.
- 03
There is no safe way to be wrong
Output with no source, no confidence, no owner, and no correction path cannot be trusted with anything consequential, so it ends up used for nothing consequential.
What the work includes, start to finish.
- 01
The narrow task, and what a wrong answer costs
Pick the specific decision or step, define what failure actually does downstream, and mark where deterministic rules should keep control instead.
- 02
Grounding, citations, and confidence
Answers drawn from your approved material with the source shown beside them, so a reviewer can check the claim rather than judge the tone.
- 03
Models inside the application
Connected to the records, the permission model, and the next step, so an output becomes an action on a record rather than text in a window.
- 04
Evaluation, monitoring, and a deterministic fallback
A fixed set of representative cases the system is scored against, quality and cost watched in production, and a defined path for when the model is unavailable or out of its depth.
What this looks like once it exists.
A source-grounded assistant
Pixelity Agent Builder answers from approved material using hybrid retrieval, cites what it used, is scored against a golden evaluation set, and falls back to a deterministic response rather than guessing.
Triage inside the queue
Incoming cases classified, prioritized, and routed with their context attached, and confirmation requested where the consequence warrants it.
Four stages, each ending in something you can see.
- 01
Map
Define the narrow task, what an acceptable failure looks like, who reviews what, and which parts must stay deterministic.
OutputUse-case and risk definition
- 02
Shape
Design grounding, citations, confidence, correction, approval, and the evaluation set the system will be judged against.
OutputPrototype and evaluation set
- 03
Build
Connect models to the application logic, records, and permissions, with monitoring and a deterministic fallback in place.
OutputAI running inside the workflow
- 04
Evolve
Improve quality, cost, and controls against real cases as the operating rules and the models themselves change.
OutputAccountable AI operations
Where this service is usually needed.
AI Document Operations
Review extracted fields beside a fictional source document, correct uncertain output, and route the approved record.
Open the demonstrationRelated concept demonstration
- 01Inbox
- 02Extraction
- 03Validation
- 04Human review
- 05Audit
Engineering decisions that protect the operation.
- 01
Choose a model on task quality, latency, privacy, and cost. A provider name is not an architecture.
- 02
Keep the source, the output, the correction, and the human decision together, so quality can be measured rather than argued about.
- 03
Wrap model output in deterministic validation and the permissions the user already has, whenever the workflow allows it.
The questions that come up before a project starts.
- Does every AI output need human review?
- No. Review should follow impact, confidence, reversibility, policy, and whether there is evidence available to check the output against. Reviewing everything costs what the work itself cost.
- Can Pixelity work with different model providers?
- Yes. Hosted or open, chosen per task on quality, privacy, latency, and operational fit, and replaceable later. The workflow, the records, and the evaluation set outlive any one model.
- Can live AI be introduced after a controlled prototype?
- Yes. A controlled prototype tests the workflow, the interface, the review decisions, and the evaluation method first, so the model is the last variable rather than the first.
Articles that help frame the decision.
Tell us the process everyone works around.
Describe it in a sentence or two. We will come back with what we would build first, what it would take, and whether you actually need us for it.