Building an AI claims automation solution for an insurance company requires a partner that combines several capabilities that rarely all exist in one generalist AI vendor: real understanding of how claims actually move through first notice of loss, triage, investigation, and settlement, technical depth across document extraction, computer vision for damage assessment, and language models for summarization and drafting, and the infrastructure experience to deploy privately when claims data includes medical records or other sensitive information that cannot go to a public API. Just as important is integration experience with the core claims system already in place, since an automation layer that cannot read from and write back to Guidewire, Duck Creek, or an equivalent platform will end up as a disconnected tool nobody actually uses in their daily workflow. A good way to evaluate a potential partner is to ask for their approach to human review checkpoints and regulatory documentation, since a vendor that treats these as optional add-ons rather than core design decisions is likely to create compliance problems later. Nanobase AI combines claims domain knowledge, document and vision AI, private deployment infrastructure, and core system integration experience to build these solutions end to end.
A due-diligence checklist before signing anything
Choosing a claims automation partner is a due-diligence exercise more than a feature comparison, since the capabilities that matter most, domain understanding of how claims actually move through a lifecycle, and infrastructure discipline for sensitive data, are hard to evaluate from a sales deck alone.
| Diligence area | What to ask for | Why it predicts success |
|---|---|---|
| Claims lifecycle understanding | Walk through how they would handle a specific claim type from FNOL to settlement | Reveals whether they understand insurance workflow or only general AI engineering |
| Deployment model options | Whether they can deploy privately or on-premise, not just via a public API | Determines fit once medical records or other sensitive data are involved |
| Core system integration experience | Specific prior work connecting to a policy or claims administration platform | A disconnected automation tool that nobody actually uses fails regardless of model quality |
| Human review architecture | Their default approach to approval checkpoints and audit logging | A vendor treating these as optional add-ons signals later compliance problems |
| Data handling terms | Where training and inference data is stored, and who owns any resulting model | Determines long-term data control and switching cost |
The single most revealing diligence question is asking a candidate partner to describe their default approach to human review checkpoints, since a vendor that treats this as optional rather than core design reveals how they think about risk generally.
Running a structured evaluation process
- Write a short, specific scope for a proof of concept before talking to any vendor, covering one claim type or one workflow segment, not the entire claims department at once.
- Request each candidate's approach to the same scoped problem in writing, comparing their proposed architecture and human review design, not just their pricing.
- Ask for the specific systems they have integrated with previously and what the integration pattern looked like, since generic AI experience without core-system integration experience is a different and weaker qualification.
- Confirm data ownership, model ownership, and exit terms in writing before the engagement starts, since these terms are far harder to negotiate favorably after a system is already in production and switching becomes costly.
- Run the proof of concept against real, representative claims data (with appropriate access controls), not a curated demo sample, before committing to a broader engagement.
Scoping a proof of concept narrowly and testing it against real, representative data, before committing to a full engagement, is what separates a sound partner selection from one based on a persuasive demo.
The generalist AI vendor trap
A recurring pattern worth watching for: a vendor with strong general AI engineering credentials but no claims-specific project history will often produce an impressive prototype that stalls once it meets the actual complexity of core system integration, regulatory documentation requirements, or the messy reality of real claims data. General AI capability is necessary but not sufficient; the domain-specific parts, understanding claims workflow, meeting compliance documentation standards, and integrating with legacy or vendor-specific systems, are what typically determine whether a pilot becomes a production system or stalls indefinitely.
A polished prototype from a generalist AI vendor is a weak predictor of production success; prior claims-specific integration and compliance experience predicts it far better. This overlaps with the underwriting-specific version of the same evaluation problem covered in choosing an underwriting automation partner, which focuses on the implementation side of a similar vendor decision.
What good references actually look like
Rather than asking for a generic reference call, ask a candidate partner to walk through a specific technical decision they made on a prior claims automation project and why, such as how they handled a particular data quality problem or designed an escalation path for an edge case. A vendor who can discuss specific technical trade-offs in detail is a stronger signal than one who can only describe outcomes at a high level, since the ability to reason through a concrete problem reflects the depth of experience that will matter once the actual engagement hits its own edge cases.
A candidate partner's ability to walk through one specific prior technical decision in detail is a better reference signal than any list of past client names.
Frequently asked questions
Should we require on-premise deployment from every claims automation vendor?
Only if the specific data involved requires it. Claims involving medical records or other highly sensitive information generally warrant private or on-premise deployment, while lower-sensitivity workflows may be fine on a well-secured cloud deployment. The requirement should follow the data sensitivity, not be applied uniformly regardless of workload.
How long should a proof of concept run before deciding on a full engagement?
Long enough to test against a representative range of claim complexity and edge cases, not just the easiest examples. A proof of concept limited to only straightforward claims will not reveal how the system handles the harder cases that make up a meaningful share of real volume.
What contract terms matter most beyond price?
Data ownership, model ownership, and exit or handover terms matter more than price differences between candidates in most cases, since a favorable price with poor exit terms can leave an insurer locked into a vendor relationship that becomes expensive to leave later.
How Nanobase AI helps
Nanobase AI combines claims domain knowledge, document and vision AI, private deployment infrastructure, and core system integration experience, and scopes every engagement around a narrow proof of concept tested against real claims data before any broader commitment. Human review architecture and audit logging are part of the initial design, not an add-on requested later.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.