Evaluating AI vendors and build-versus-buy decisions

Why this decision is harder than it looks

Professional services firms (law, accounting, consulting, advisory) sell judgment, not just software. That changes the calculus vendors don't always highlight:

• Client confidentiality obligations (attorney-client privilege, auditor independence rules) restrict where data can go.
• Domain accuracy matters more than general fluency. A tool that summarizes contracts well but misses a jurisdiction-specific liability clause creates real professional risk.
• Existing systems (practice management software like Clio or iManage, or audit platforms) already hold the firm's data. A new tool that doesn't talk to them creates duplicate work, which kills adoption.

The build-versus-buy question is really three questions in disguise: is this a capability we need to control, can we build it credibly, and does the vendor's answer to the first two actually hold up under scrutiny.

The evaluation scorecard

Use a simple weighted scorecard when comparing a vendor pitch to an internal build. Score each dimension 1 to 5, weight by importance to your practice, and total it. The scorecard forces the same rigor on both options, since internal projects often skip the scrutiny vendors get.

1. Data security and confidentiality

• Where does data go? Is it processed by a third-party model provider, and under what contract terms?
• Is there a data processing agreement (DPA) and, for EU clients, GDPR (General Data Protection Regulation) compliance?
• Does the vendor train its models on your data by default? (Many enterprise contracts now exclude this, but check the actual clause, not the marketing page.)
• For a build: who inside the firm controls access, and is there an audit trail?

2. Accuracy on domain-specific tasks

• Ask for benchmark results on tasks resembling yours, not generic leaderboard scores. A contract-review tool's performance on NDAs tells you little about its performance on cross-border M&A due diligence.
• Request a pilot with your own redacted documents, not vendor demo data.
• Check for hallucinationhallucinationA hallucination is when an AI model generates output that is fluent and confident but factually wrong, fabricated, or unsupported by its source data.View full definition → rates on citation-heavy tasks. Legal research tools have had well-documented failures here, including sanctioned court filings citing fabricated cases (a widely reported issue since 2023).
• For a build: what model are you calling, and has anyone validated it against your document types?

3. Integration with practice management systems

• Does the tool have a documented application programming interfaceapplication programming interfaceApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → (APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →, a defined way for software systems to exchange data) that connects to your existing case management, billing, or document management system?
• Will it require manual export/import, which staff will quietly abandon within weeks?
• For a build: integration is usually the most underestimated cost. A "simple" internal tool that can't pull matter data automatically becomes shelf-ware.

4. Vendor viability and lock-in

• How long has the vendor operated, and who are reference clients in your practice area?
• What happens to your data and workflows if the vendor is acquired or shuts down? (Legal tech has seen real consolidation, e.g., Thomson Reuters' acquisition of Casetext in 2023.)
• For a build: what happens if the one engineer who built it leaves?

5. Total cost of ownership

• Vendor: subscription plus implementation plus training plus internal admin time.
• Build: development plus ongoing model APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → costs plus maintenance plus opportunity cost of IT time.

A worked comparison

Take the contract review example. Rough estimates, clearly labeled:

Vendor option: $150,000/year, implementation support included, accuracy benchmarked (per vendor claims) at roughly 85 to 90% agreement with senior associate review on standard commercial contracts (estimate, vendor-reported, always verify independently).

Build option: $40,000 one-time build using a general-purpose LLMLLMA Large Language Model is an AI system trained on vast text data to predict and generate language, enabling tasks like writing, summarizing, and answering questions.View full definition → APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → (cost estimate for developer time and APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → usage, 2025 to 2026 pricing), plus an estimated $15,000 to $25,000/year in ongoing APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → costs and maintenance. Accuracy unknown until tested, likely lower initially without domain fine-tuningfine-tuningFine-tuning adapts a pre-trained model to a specific task or domain by continuing training on a smaller, targeted dataset, improving accuracy and style for that use case.View full definition → or retrieval-augmented generation (RAG) grounding it in the firm's own precedent library.

Simple three-year total cost of ownership (TCO) comparison:

• Vendor: $150,000 x 3 = $450,000
• Build: $40,000 + ($20,000 x 3) = $100,000

On cost alone, build wins by roughly $350,000 over three years (illustrative estimate, not a forecast). But this ignores the scorecard's other four dimensions. If the build's accuracy sits at 65% instead of 85%, the firm pays elsewhere: partner time re-checking output, client trust risk, potential malpractice exposure. Cost comparisons without an accuracy and risk adjustment are incomplete, and this is the single most common mistake in these decisions.

When to build, when to buy

Buy when:

• The task is common across the industry (contract review, e-discovery, transcription) and mature vendors exist with track records. Examples in legal tech include Harvey, Relativity, and Luminance; in accounting, tools built on top of major ERP vendors' AI add-ons.
• Your firm lacks in-house AI engineering capacity to maintain a model pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.View full definition → responsibly.
• Speed to deployment matters more than customization.

Build when:

• The workflow is unique to your firm's practice (a niche regulatory area, a proprietary methodology) and no vendor addresses it well.
• You have technical staff who can maintain the tool long-term, not just launch it.
• Data sensitivity is high enough that even vetted third-party processing is a hard no (rare, but real for some government or defense-adjacent work).

Hybrid (increasingly common in 2026): license a foundation model APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → (from OpenAI, Anthropic, Google, or similar) but build the retrieval layer and integration in-house. This gives model quality without full vendor lock-in, at the cost of needing real technical ownership internally.

Running a real pilot, not a demo

Whichever path you lean toward, insist on a pilot with your own dataown dataData collected directly from your own customers and prospects through your own channels: your most reliable and privacy-compliant source.View full definition → before signing anything multi-year:

• Use redacted or anonymized real matters, not vendor sample data.
• Have the professionals who'll actually use the tool (associates, not just partners) score outputs blind, comparing AI output against human-done work without knowing which is which.
• Set a numeric bar in advance: for example, "output must match senior reviewer judgment on at least 80% of clauses flagged" (illustrative threshold, set your own based on risk tolerance).
• Time-box it: 30 to 60 days, then a formal go/no-go with the scorecard totals in front of the partners.

For a plain-language primer on evaluating AI system reliability before deployment, the NIST AI Risk Management Framework (US National Institute of Standards and Technology) is a solid, free, non-vendor resource applicable well beyond government use.

🎬 [VIDEO: "Build vs Buy: The AI Decision Every Company Faces" - youtube.com - a practical framework for weighing internal AI development against vendor solutions, applicable across professional services contexts]

Key Takeaways

• Score vendor and build options on the same four dimensions: data security, domain accuracy, integration, and total cost of ownership. Don't let vendors set the terms of comparison.
• Cost comparisons are incomplete without an accuracy and risk adjustment. A cheaper tool that's wrong more often can cost more in partner review time and client risk.
• Buy for common, mature use cases (contract review, e-discovery); build only when the workflow is genuinely unique and you have staff to maintain it long-term.
• Always pilot with your own real (redacted) data before signing a multi-year contract or greenlighting a build. Demo performance and production performance are different things.
• Hybrid approaches, licensing a foundation model but building your own integration layer, are increasingly the practical middle ground in 2026.