Selecting an AI vendor for voice, document, or decisioning use cases involves more than comparing feature lists. This FAQ helps procurement, IT, and operations teams in BFSI, healthcare, government, and insurance ask the right questions before signing a contract.
1. What should we prioritise first when evaluating an AI vendor: features or use-case fit?
Use-case fit should come before a feature checklist, because a vendor with an impressive feature list that hasn't solved your specific problem before will still require significant customisation risk. A vendor that has deployed voice AI for loan collections calls understands compliance nuances, escalation patterns, and vernacular requirements specific to that use case in ways a generalist platform may not. Ask prospective vendors for examples of deployments with genuinely comparable use cases and industry context — not just adjacent ones — and weigh that experience heavily against a longer feature list that hasn't been proven in your specific operational reality.
2. How do we verify a vendor's claims about accuracy and performance before committing?
Verify claims by requesting a pilot on your own data and real scenarios rather than relying on a vendor's demo or generic benchmark numbers. A demo optimised for a sales call rarely reflects how a system performs against your actual customer base, accents, document formats, and edge cases. Ask for a proof-of-concept period using anonymised or sample data from your own operations, with clearly agreed success metrics defined upfront — resolution rate, accuracy on your specific document types, or containment rate on your call volumes. Also ask for reference calls with existing clients in a similar industry, and ask those references directly about performance gaps, not just successes.
3. What integration capabilities should we look for in an AI platform?
Look for a platform with proven experience integrating with the specific systems you run — core banking software, hospital information systems, CRM platforms, or government case management tools — rather than generic API documentation alone. Ask specifically how the vendor has handled integration with systems similar to yours in the past, what authentication and data exchange standards they support, and how long integration typically takes for a comparable setup. A platform that claims broad compatibility but has never actually connected to your type of legacy system carries meaningfully more implementation risk than one with direct, demonstrable experience.
4. How important is multilingual and regional language support when choosing a vendor?
For almost any Indian deployment, multilingual support should be a primary evaluation criterion, not an afterthought, given India's linguistic diversity and the fact that a large share of customers are more comfortable in a regional language than in English or Hindi. Ask vendors specifically which languages are natively supported versus translated, how dialect variation within a language is handled, and whether they have live deployments actually running in the languages your customer base needs — not just a roadmap promise. A vendor that treats regional languages as a future feature rather than a current, proven capability is a meaningful risk if your customer base skews toward Tier 2 and Tier 3 markets.
5. Should we choose a specialised AI vendor or a large generalist technology provider?
The right choice depends on how specific your use case is and how much you value deep domain expertise versus broad ecosystem integration. A specialised vendor focused on voice AI or document AI for BFSI and healthcare typically brings sharper domain understanding — compliance nuances, industry-specific terminology, common escalation patterns — that a generalist provider covering dozens of unrelated industries may lack. A large generalist provider might offer broader platform integration if you're already deep in their ecosystem. Many organisations find that a specialised vendor delivers faster time-to-value for a well-defined use case, while generalist platforms make more sense when AI is one small piece of a much larger technology consolidation strategy.
6. What questions should we ask about data security and compliance during vendor evaluation?
Ask where data is stored and processed, who has access to it, how long it is retained, whether it is used to train models shared across other clients, and what certifications or compliance frameworks the vendor adheres to. For BFSI and healthcare specifically, ask directly whether the vendor's infrastructure and practices are designed to support RBI, IRDAI, or healthcare data protection expectations relevant to your sector. Also ask about the vendor's incident response process — what happens, and how quickly you're notified, if a security issue occurs. A vendor that answers these questions vaguely or defers them to "we'll figure that out during implementation" is a red flag.
7. How should pricing models factor into choosing between AI vendors?
Pricing model fit matters as much as the headline price, because per-call, per-user, and platform-fee models create very different cost dynamics depending on your volume and growth pattern. A per-call pricing model may be economical at moderate volume but become expensive at very high scale, whereas a platform licensing fee might be better value once you cross a certain volume threshold. Model your expected usage against each vendor's pricing structure over a 12- to 24-month horizon, not just the initial quoted rate, and ask vendors to be transparent about what triggers cost increases — additional languages, higher call volumes, or extra integrations.
8. What level of ongoing support and customisation should we expect after go-live?
Expect a good vendor to provide continued tuning, monitoring, and support well beyond initial go-live, since AI systems need ongoing refinement as products, policies, and customer patterns change. Ask specifically what the support model looks like after launch — is there a dedicated account or technical contact, how are model updates handled when your product or policy terms change, and what the typical turnaround time is for fixing an identified accuracy issue. Vendors who treat go-live as the finish line rather than the starting point of an ongoing relationship tend to leave clients managing degradation on their own within a few months.
9. How do we compare vendors that require a data science team versus those that don't?
Compare based on your organisation's actual internal capacity — a platform requiring an in-house data science team to configure, monitor, and retrain models adds real operational overhead that many mid-size BFSI, healthcare, or government organisations don't have readily available. Vendors offering managed, low-code, or fully hosted configurations let business and operations teams manage the AI system directly without needing specialised technical staff, which matters if you don't plan to build a dedicated AI team. Ask directly what skills are needed on your side post-implementation, and whether the vendor provides training or managed services to fill any gap.
10. What red flags suggest an AI vendor may not be a good long-term fit?
Watch for vagueness about past deployments in your specific industry, reluctance to provide client references, resistance to a data-backed pilot before contract signing, unclear answers about data security and residency, and pricing structures that aren't transparent about scaling costs. Also be cautious of vendors who present AI as a complete replacement for human oversight rather than acknowledging where human review remains necessary — this often signals unrealistic expectations that surface as problems after deployment. A vendor that is transparent about limitations and willing to start with a scoped pilot rather than pushing for a large upfront commitment is generally a stronger long-term partner.
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