From fragmented paperwork
to decisions that hold up.
Intlix brings policy, customer, provider and document context into governed AI workflows — so underwriters, adjusters and service teams spend their time on the calls only they should be making.
How we create value
Industry context changes the engineering.
The operating friction
Insurance work moves through clauses, provider evidence, customer history and exception queues. The information a decision needs usually arrives in pieces, and rarely all at once.
We use that friction to define the context, integration and controls the engineering has to solve — rather than handing over a generic list of use cases.
How Intlix intervenes
Engineer backward from the outcome.
Start from the decision
Map the workflow, the people in it, the systems it touches, and the measure it is judged on.
Assemble the context
Connect the data, documents and systems that decision actually depends on.
Put intelligence in the path
Apply models, agents and automation where they improve the call — not wherever they fit.
Run it in the open
Keep human authority, evaluation, observability and running cost visible after go-live.
The operating journey
Six steps, one accountable chain.
Intake
Customer, broker or provider signal
Extract
Documents and structured evidence
Validate
Policy, eligibility and consistency
Review
Underwriter and adjuster authority
Settle
Approved servicing or settlement action
Govern
Evidence, controls and economics
What we build
Four engineering motions.
One operating outcome.
Modernize policy and claims data
Secure data products, document stores, integration and metadata.
AI and operations work from the same policy context.
Engineer document and decision intelligence
Extraction, summarization, clause retrieval and discrepancy detection.
Specialists get decision-ready evidence, not another queue.
Orchestrate approved workflows
Intake, underwriting, servicing, claims and exception handling.
Automation stops where policy or judgment requires a person.
Operate with evidence
Evaluation, traceability, privacy controls and resilience.
Every recommendation can be understood after the fact.
Let's talk about your insurance workflows.
Bring us the decision that is slow, manual or hard to defend. We will map what it would take to engineer it properly.
Start the conversationSector questions
What insurance teams
ask us first.
How do you avoid bias in underwriting models?
By testing for disparate outcomes across protected characteristics as part of the evaluation set, before deployment and continuously after. A model that cannot pass that test does not ship.
Can AI make claims decisions?
It can assist them. We design so that automation handles triage, evidence gathering and the straightforward cases, while declines and complex settlements keep a human decision-maker with the full context.
How does this fit our policy administration system?
As an integration, not a replacement. Most recoverable value sits in the workflow, the document handling and the decision support around the system of record.
What about regulatory reporting?
The evidence a regulator asks for is generated as a by-product of the process rather than assembled retrospectively, which is where most reporting cost and error originates.
How do you handle unstructured claim evidence?
Documents, images and correspondence are extracted into structured, cited evidence that an assessor can verify against the source rather than having to trust a summary.