How to Evaluate an AI Development Partner: A Buyer's Checklist for Indian Enterprises

The AI Vendor Selection Problem in India

Every IT services company in India now claims AI capabilities. A LinkedIn search for AI development company Bangalore returns over 2,000 results. The problem is not finding a vendor; it is distinguishing the 50-odd companies that have genuinely delivered production AI systems from the 1,950 that rebranded their web development practice with an AI label after ChatGPT went viral. The cost of choosing wrong is not just the $30,000-80,000 in wasted development fees. It is the 4-6 months of lost time, the organizational credibility damage when the project fails, and the technical debt left behind that the next team has to dismantle before starting over.

This checklist is designed for CTOs, VPs of Engineering, and technical procurement leads at Indian enterprises evaluating AI development partners. It is organized as a scoring framework: evaluate each vendor across the criteria below, score them 1-5 on each dimension, and use the weighted total to make a data-driven selection.

Technical Depth Assessment (Weight: 30%)

RAG Architecture Knowledge

RAG is the most commonly requested AI project type in 2026, and it is also where the gap between genuine expertise and superficial knowledge is most visible. Ask the vendor: What chunking strategy would you recommend for our document types, and why? A competent answer references specific strategies (fixed-size, semantic, document-aware, parent-child) with trade-offs for your specific documents. An incompetent answer is a generic statement about splitting text into chunks. Follow up with: How do you handle tables in PDF documents during ingestion? A strong answer describes table detection, structured preservation as markdown or HTML, and embedding tables as complete units. A weak answer either ignores the question or suggests OCR as a complete solution.

Ask about evaluation: How do you measure RAG quality in production? The answer should reference specific metrics like retrieval precision and recall, RAGAS faithfulness scores, LLM-as-judge patterns, and continuous monitoring. If the vendor's quality assurance plan is manual testing with a spreadsheet of test questions, they have not built a production system. Every team that has shipped RAG to real users has learned that systematic evaluation is non-negotiable.

LLM Integration and Prompt Engineering

Ask: Which LLM models have you used in production, and what were the selection criteria? A knowledgeable vendor discusses specific model trade-offs: Claude Sonnet for complex reasoning at moderate cost, GPT-4o-mini for high-volume simple tasks, Claude Haiku for cost-sensitive classification. They should mention context window implications for your use case, structured output requirements, and whether tool use or function calling is needed. Ask about prompt engineering methodology. Vendors with production experience describe iterative prompt development with version control, A/B testing prompts against evaluation datasets, and managing prompt drift as models update. The key question: Can you show me an evaluation dataset and results from a previous project? If they cannot show any systematic evaluation artifacts, their prompt engineering process is ad hoc.

Fine-Tuning and Model Customization

Most AI projects in 2026 do not need fine-tuning, as RAG and in-context learning handle the majority of customization needs. But if your use case requires it, the vendor should explain when fine-tuning is and is not appropriate. A strong answer: Fine-tuning makes sense when you need to change the model's behavior or output style consistently across thousands of interactions, and when you have at least 500-1,000 high-quality training examples. For domain-specific knowledge, RAG is almost always more cost-effective and easier to maintain because you can update the knowledge base without retraining. If the vendor recommends fine-tuning as the first solution for every problem, they likely lack experience with production RAG and are falling back on what they know from pre-2024 AI development.

Agent and Orchestration Experience

If your project involves AI agents, ask: Describe the orchestration architecture of the last agent system you built. A credible answer discusses: the state management approach (conversation memory, workflow state), tool calling patterns and error handling when tools fail, human-in-the-loop checkpoints for critical decisions, and how they handle the agent deviating from the expected workflow. The most telling question: What was the hardest failure mode you encountered in an agent project, and how did you solve it? Vendors with real agent experience have war stories about cascading tool failures, infinite loops in reasoning, and edge cases where the agent made decisions it should not have. Vendors without experience give textbook answers about LangChain agent classes.

Team Composition and Quality (Weight: 25%)

Meeting the Actual Team

The single most important evaluation step is meeting the engineers who will actually work on your project, not the sales team, not the company's CTO who will not write a line of code, but the ML engineer and backend developer who will be building your system. Request a technical deep-dive meeting with the proposed team where they walk through a recent project architecture. Evaluate: Can the ML engineer explain embedding model trade-offs without reading from slides? Can the backend developer describe how they handle streaming LLM responses and error states? Does the team ask intelligent questions about your requirements, or do they agree with everything you say? A team that pushes back on some of your assumptions is demonstrating expertise. A team that nods along to everything is either inexperienced or telling you what you want to hear.

Team Stability and Retention

A common failure mode with Indian IT services companies is the bait-and-switch: senior engineers participate in the sales process and early architecture, then get pulled to other projects and replaced with junior developers. This is devastating for AI projects because the architecture decisions made in weeks 1-3 require the same level of expertise throughout the project. Ask directly: Will the engineers I meet today be the ones working on my project for its full duration? What is your team's attrition rate for AI projects? Can I get a contractual commitment that the proposed senior engineer stays on my project? A vendor willing to make contractual commitments about team composition is significantly more reliable than one that gives verbal assurances.

Technical Leadership Quality

The technical lead or architect for your project should have at least two production AI systems under their belt, meaning systems that real users interact with daily, not internal prototypes or hackathon projects. Ask for specifics: What was the daily query volume? How did you handle quality degradation over time? What was the production uptime? What architecture decisions would you make differently in hindsight? The quality of the technical lead determines the quality of the architecture, and the architecture determines whether the system works at scale or collapses under real-world conditions.

Portfolio and Case Study Review (Weight: 20%)

Red Flags in Portfolios

Watch for these red flags when reviewing an AI vendor's portfolio. Generic descriptions without technical specifics: "We built an AI chatbot for a leading bank" tells you nothing. A credible case study mentions the LLM model used, the integration points, the evaluation metrics achieved, and the query volume handled. All projects are POCs or prototypes with no production metrics: building demos is fundamentally different from building systems that serve thousands of users daily. Every case study uses different technology: a vendor that used LangChain on one project, Semantic Kernel on the next, and a custom framework on the third may be experimentally exploring tools rather than having deep expertise in any of them. No mention of evaluation, monitoring, or production operations: if the case study ends at deployment without discussing how quality is maintained post-launch, the vendor likely hands off projects and moves on, leaving you to handle production issues alone.

What to Look For in Strong Case Studies

Strong case studies include: specific technology choices with reasoning ("We chose pgvector over Pinecone because the client's data residency requirements prohibited cloud-managed vector databases"), quantitative results (retrieval precision, latency percentiles, user satisfaction scores), production metrics (daily query volume, uptime, error rates), description of challenges encountered and how they were resolved, and post-launch support and optimization activities. A vendor that can walk you through one project in this level of detail, answering follow-up questions about decisions and trade-offs, has actually built the system. A vendor that gives surface-level descriptions of multiple projects may be embellishing their involvement.

Reference Checks That Matter

Request two to three references and call them. Do not just ask "Was the vendor good?" Instead ask: What was the original scope and timeline, and how did the actual delivery compare? Were the senior engineers who participated in the sales process the same ones who built the system? How did the vendor handle unexpected technical challenges or scope changes? Is the system they built still in production? If so, what maintenance has been required? Would you use this vendor again for your next AI project? The answer to the last question is the most valuable data point in the entire evaluation process.

Pricing and Commercial Terms (Weight: 15%)

Understanding Pricing Models

AI development vendors in India typically offer three pricing models. Time and material at $35-65/hour blended rate gives maximum flexibility but uncertain total cost. Fixed price at a 15-25% premium over estimated T&M cost provides budget certainty but shifts scope management risk to the vendor, who may cut corners to stay within budget. Sprint-based hybrid pricing starts with a fixed-price discovery and MVP phase followed by T&M sprints for iteration. The sprint-based model aligns best with AI project realities because AI development is inherently iterative: you cannot fully specify the system behavior upfront, and optimization requires experimentation with real user data.

Pricing Red Flags

Be wary of quotes that are 50% or more below the market range. A production RAG system quoted at $15,000 when market rates are $30,000-70,000 is either dramatically under-scoped, planning to use junior engineers and mark them as senior, or not including critical components like evaluation, monitoring, and documentation. Equally concerning is a vendor that cannot provide a cost breakdown by phase and role. If you ask "how many hours of ML engineering versus backend engineering are in this quote" and they cannot answer, the estimate was not built from a real project plan. Finally, watch for vendors that do not separately estimate infrastructure and API costs. These are ongoing costs you will bear after the project ends, and they should be part of the proposal so you budget accurately.

IP Ownership and Code Rights

This is non-negotiable: you must own 100% of the code, models, and data produced during the engagement. Specifically, the contract should state that all custom code written for your project is your intellectual property upon full payment, that any training data, fine-tuned model weights, or evaluation datasets are your property, that the vendor may retain the right to use general knowledge and non-project-specific tools but not your proprietary components, and that source code is delivered in a version-controlled repository you own with full commit history. Some vendors try to retain IP rights to reuse components across clients. This is acceptable for truly generic components like deployment scripts or monitoring templates, but not for business-logic-specific code, prompts, or data pipelines.

Security and Compliance Practices (Weight: 10%)

Data Handling During Development

Ask the vendor: How do you handle our production data during development and testing? A mature answer describes: using anonymized or synthetic data for development wherever possible, secure access controls limiting which team members can access production data, data handling agreements specifying that client data is deleted from development environments after project completion, logging and audit trails for data access, and separate development environments that do not connect to production systems. For BFSI and healthcare enterprises, ask additionally about: compliance with RBI's outsourcing guidelines for technology services, DPDP Act 2023 compliance for personal data processing, whether the vendor has ISO 27001 or SOC 2 certification, and how they handle data residency requirements.

LLM Security Considerations

AI systems introduce security concerns that traditional software does not. Ask the vendor about: prompt injection prevention (how they prevent users from manipulating the LLM into revealing system prompts or behaving unexpectedly), data leakage through LLM APIs (whether they use zero-data-retention API options from providers like Anthropic and OpenAI to prevent training data extraction), output guardrails (how they prevent the LLM from generating harmful, biased, or inappropriate content), and PII handling in prompts (how they ensure personal information is not sent to external LLM APIs when not necessary for the task). A vendor that has not thought about these issues has not built AI for enterprise environments.

Post-Launch Support and Knowledge Transfer (Weight: 10%)

Knowledge Transfer Plan

AI systems require ongoing maintenance: prompt optimization as user behavior evolves, knowledge base updates, model upgrades when providers release new versions, and evaluation dataset expansion. The vendor's knowledge transfer plan should include: comprehensive architecture documentation with decision rationale, recorded walkthrough sessions of all major system components, runbooks for common operational tasks (updating the knowledge base, retraining evaluation datasets, scaling infrastructure), a defined handover period of 2-4 weeks where the vendor supports your team taking over operations, and documentation of all third-party service accounts, API keys, and access credentials.

Support Models After Launch

Evaluate the vendor's post-launch support options: Do they offer a retainer for ongoing optimization at a reduced rate? What is the response time SLA for production issues? Can they provide on-call support for the first month after launch? Is post-launch support priced separately from the project or bundled? A vendor that structures the engagement with a clean break at deployment may be optimizing for their utilization metrics rather than your success. The best vendors offer a transition period where support tapers off as your team gains confidence.

The Evaluation Scorecard

Score each vendor on a 1-5 scale for each category, then multiply by the weight. Technical Depth (30%): RAG architecture knowledge, LLM integration experience, evaluation methodology, and agent orchestration capability. Team Quality (25%): meeting the actual team, stability commitments, and technical leadership depth. Portfolio (20%): production case studies with metrics, technology consistency, and strong reference checks. Commercial Terms (15%): pricing transparency, IP ownership clarity, and fair contract structure. Security and Support (10%): data handling practices, LLM security awareness, and post-launch support plan. A vendor scoring above 4.0 weighted average is a strong candidate. Between 3.0 and 4.0 is acceptable with specific areas to negotiate. Below 3.0 suggests significant risk that is unlikely to be mitigated by contract terms alone.

What Boolean Beyond Brings to Each Evaluation Criterion

We built this checklist from the buyer's perspective, but it is fair to address how Boolean Beyond measures against it. On technical depth: our team has deployed 14 production RAG systems with documented evaluation metrics, and every project includes a RAGAS-based evaluation pipeline as standard deliverable. On team quality: we assign a dedicated technical lead for the full project duration with contractual commitment, and you meet the exact engineers who will build your system. On portfolio: we provide detailed case studies with architecture decisions, production metrics, and reference contacts upon request. On pricing: we use the sprint-based hybrid model with transparent role-based cost breakdowns. On IP: you own 100% of custom code and data, delivered in a Git repository you control. On security: we use zero-data-retention LLM API configurations by default and can work within your VPC for data-sensitive projects.

Common Mistakes in AI Vendor Selection

Choosing the Cheapest Option

AI development is not like building a CRUD application where the primary variable is coding speed. AI projects have high variance in outcome quality: a senior ML engineer making the right architecture decision in week two saves ten weeks of rework later. The cheapest vendor, often staffing projects with mid-level engineers at $25-30/hour, may deliver a system that technically works in the demo but fails under production conditions. The cost of rebuilding is typically 1.5-2x the original project cost because the new team must understand what was built, identify the architectural issues, and often start critical components from scratch.

Skipping the Technical Deep-Dive

Some procurement processes evaluate AI vendors primarily on company credentials, client logos, and pricing, treating AI development like commodity IT services. This approach fails because AI project success depends on individual engineer expertise, not company brand. A 20-person AI company with three deeply experienced ML engineers will outperform a 2,000-person IT services company that recently formed an AI practice. The technical deep-dive, where the proposed team walks through their architecture approach for your specific problem, is the most predictive evaluation step. Companies that skip it based on brand name alone fail at a rate we estimate at 40-50% for non-trivial AI projects.

Not Defining Success Metrics Upfront

Before engaging any vendor, define what success looks like in measurable terms. For a RAG system: target retrieval precision above 0.75, answer faithfulness above 0.85, and response latency under 4 seconds at p95. For a chatbot: resolution rate above 60% without human handoff, user satisfaction score above 4.0 out of 5, and containment rate for sensitive topics at 100%. These metrics become acceptance criteria in the contract. Without them, project completion becomes a matter of opinion rather than measurement, which is how projects end up in disputes about whether the delivered system meets expectations.