The Navata Method: What to Test Before You Hire or Switch a Veeva or AI Advisory Firm

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Credentials tell you a firm belongs in the room. They don't tell you whether it can carry a consequential Veeva or AI decision through to something you can still defend once it's gone.

The four-stage evaluation to run on any Veeva or AI advisory firm, whether you are choosing one for the first time or weighing an alternative to the one you already have. Ours included.

Buyers comparing Veeva consulting firms or AI advisory firms usually have plenty of evidence about the firm itself: certifications, client references, implementation history, partner status, years of specialist experience. What they rarely have before signing is evidence about the thing the firm is actually being hired to shape: a consequential decision.

A firm can deliver the agreed scope, complete the configuration, assemble the validation package, and reach go-live on schedule, and still leave the client with an architecture nobody can adequately defend eighteen months later. That's the capability conventional vendor selection struggles to expose. Credentials filter out firms that don't belong in the conversation at all, but they don't test whether this specific firm can carry a specific, consequential decision through to something your organisation can still defend once the firm is gone.

This is a different question from how a firm behaves once it's already been engaged, which I've written about elsewhere. That question assumes the firm has already been chosen. This piece is about the decision before that one: how you know, before signature, whether a firm, whatever kind of firm, can do the work at all.

Investigate, Architect, Prove, Own

I use a four-stage test to evaluate a Veeva or AI advisory firm: the Navata Method, the same discipline that structures how we run our own engagements and the offers we have built around it. Investigate, Architect, Prove, Own describes what a firm should be able to demonstrate at each stage of a regulated technology decision, from establishing what is actually true, through designing and defending an architecture, to proving the result holds up and leaving the client able to run it without the firm in the room. Each stage should leave behind its own piece of evidence: a decision basis, a decision record, an acceptance basis, and an ownership record. Together they are the paper trail for one decision, rather than four separate exercises.

Applying it as a buyer's test, rather than only a delivery method, is a natural extension, and an uncomfortable one: it means Navata should be evaluated against exactly the same four questions. That is the point. A framework only earns the name when it holds up under its own scrutiny too.

The same test applies to different advisory models

This applies whether you're choosing a firm for the first time or already have one and are weighing an alternative. The four tests run forward, on a firm you haven't yet hired, and backward, on one you already have. If you're in the second situation, the section further down on finding an alternative goes into that directly.

It also applies whatever kind of firm sits across the table: a systems integrator, a boutique Veeva consultancy, an independent Veeva Vault consultant or architect working solo, or an AI-focused advisory firm. Different operating models raise different questions worth investigating, including staffing depth, specialisation and continuity after the engagement ends, but archetype alone doesn't establish capability. Scale and specialisation are both compatible with passing all four tests, and both compatible with failing them. The four things worth testing stay the same regardless of which kind of firm is across the table.

More on how Navata approaches AI-specific architecture →

The test also works differently from a repackaged RFP scorecard. A vendor-selection matrix scores whether a firm has done similar work before: years of experience, project count, named clients, certifications held, all backward-looking proxies for general competence. This test targets something narrower: how a firm reasons under the specific kind of uncertainty a regulated Veeva or AI decision creates, where the cost of being wrong is a decision nobody can defend at inspection. A firm can score well on every line of a procurement scorecard and still fail all four of these tests, because a procurement scorecard and an architecture-reasoning test measure separate things.

Investigate: does the firm establish what is true before it proposes anything

The first test is about restraint: whether a firm can resist proposing a solution before it has actually looked.

A weak investigation looks like this: the firm's discovery phase produces a summary slide within a week of kickoff, built mostly from the RFP document and a handful of stakeholder interviews. It reflects your own stated requirement back to you, dressed in the firm's methodology language, and calls that analysis. A genuine investigation looks different. It asks to see your actual configuration, the running system itself, rather than the process document describing what it's supposed to do. It asks for your actual validation evidence, the executed records themselves, rather than a summary of your validation approach. It asks who is affected by the process beyond the people who joined the sponsor conversations, because the people living with a workaround every day often know more about where a process actually breaks than the people who approved its design.

Ask a prospective firm directly what it wants to see before it will recommend anything. A strong answer names specific artefacts: source records, process variants across sites, prior decisions and the exceptions carved out of them, actual system behaviour rather than documented behaviour, and who currently holds authority over the decisions that follow. A weak answer moves straight from your stated requirement to the firm's preferred service line, because it has nothing else to offer yet and would rather that didn't show.

There is a second, sharper question worth asking alongside the first: what evidence would change your view. Advisory work exists to reduce decision uncertainty, and a firm whose diagnosis survives any amount of contrary evidence is defending a conclusion it reached before the engagement started, dressed up as an investigation. I would treat a confident, unmovable answer here as a warning sign rather than reassurance.

Consider an illustrative pattern, not a real client: a request arrives as “we need a new deviation workflow.” Taken at face value, that is a configuration task. Investigated properly, it often turns out to be a disagreement between a global process standard and a local escalation requirement that nobody has actually adjudicated, just routed around with manual exceptions for two years. A firm that starts configuring a new workflow without surfacing that disagreement has solved the request you wrote down instead of the problem underneath it. This is premature solution commitment: committing to a fix before the investigation that would justify it has actually happened. The same pattern shows up on the AI side. “We need an AI assistant to answer SOP questions” sounds like a build task. Investigated properly, it usually surfaces document applicability gaps, obsolete content still live in the corpus, weak metadata that will make retrieval unreliable, and an undefined boundary for how much a reviewer is expected to rely on the assistant's answer versus verify it independently. Skip that investigation and you get a fluent, confident assistant answering from source material nobody checked was still current.

The evidence this stage should leave behind is more than a slide. Call it the decision basis: what the organisation is actually trying to achieve, what has to be demonstrable at the end, which material uncertainties remain open, and who has the authority to close them. That basis belongs to the client regardless of whether the firm gets hired. A firm that struggles to produce it, or treats it as proprietary methodology it will only reveal after signature, has told you something about how it operates.

More on how the Method structures this in practice →

Architect: can the firm turn intent into a decision it can defend

Investigation establishes what needs to be solved. Architecture is the harder discipline of turning that into an explicit, defensible decision about how it gets solved, with the reasoning attached rather than left implicit in a diagram.

The test I would run here is practical rather than conversational: give the prospective firm one real design tension and watch how it reasons through it live, rather than asking it to describe its approach in the abstract. A representative example: Quality wants a single global deviation process, three manufacturing sites each have a genuinely different escalation requirement driven by different local authorities, the existing Vault security model is already role-heavy from years of incremental changes, and an AI summarisation capability may eventually need to consume the resulting records. Ask the firm to talk through how it would resolve that, in the room, rather than in a follow-up document.

What separates a strong answer from a weak one is specific. A strong answer separates genuine process intent from platform convenience, rather than defaulting to whatever the tool makes easiest to configure. It distinguishes a local requirement that reflects a real regulatory difference from one that is really just historical preference nobody has revisited. It treats the security model as an architecture decision rather than a configuration option, because a security model that changes who can see or approve a record changes data access, validation scope, and operating ownership all at once, well beyond a screen layout. It names the alternative design it rejected and explains why, rather than presenting its recommendation as the only reasonable option. And it says plainly who should hold the authority to make this call, because an architecture decision without a named owner tends to get made by default, by whoever configures the system first.

As a working test, a decision earns architecture-level treatment when it touches:

• who has authority over it
• what evidence it depends on
• what a field or record is actually understood to mean
• who can access or approve it
• what happens to validation status if it changes
• who owns it operationally afterward
• what dependency it creates on a specific supplier or model
• how reversible it actually is

A choice that touches none of those is probably a configuration decision, whatever the vendor's documentation calls it.

A diagram of objects, workflows, and integrations is a useful artefact, and a limited one. Architecture is the reasoning that produced the diagram. A firm that can only walk you through the finished picture, skipping the decision points along the way, has shown you a deliverable rather than a capability.

AI adds to this requirement rather than simplifying it. An architecture that includes an AI component needs explicit treatment of:

• where its content comes from and how currency is enforced
• what it is and is not permitted to retrieve
• what permissions govern who can see its output
• what dependency the design creates on a specific model or supplier
• where a human reviewer sits in the workflow and what they are actually expected to verify
• what gets logged and how long evidence is retained
• what authority the system's output is actually given once it reaches a regulated record

A firm that can describe what a model is capable of, in the abstract, but goes quiet on these boundaries has described a technology rather than designed a system you can defend.

The evidence this stage should produce is a decision record: the decision itself, the alternative that was considered and rejected, the reasoning, the accountable owner, and the condition under which it should be revisited. That record is a client-owned artefact, separate from the configuration itself, and it is what lets someone other than the firm explain, a year later, why the system looks the way it does. None of this means every configuration choice on a programme needs this level of ceremony. The rigour should scale to how consequential and how hard to reverse the decision actually is: a field label rarely needs it, a security model that governs who can approve a batch record always does.

Prove: what would convince the firm its own recommendation is wrong

This is the stage worth spending the most time on during selection, because it's where the structural conflict of interest in advisory work is easiest to hide.

Name the risk plainly: the same firm that designed the solution can also choose the evidence used to judge it, then declare the result a success. Nobody involved needs to act in bad faith for this to happen quietly. It's simply what happens by default when the party proposing the answer is also the party marking its own homework, and most engagements never introduce anything to correct for it.

Ask about proof before the engagement starts, well ahead of delivery. If the firm recommends a particular security architecture, ask what evidence would demonstrate it works as intended, what negative scenario it would specifically test for, and what result would count as a failure rather than a footnote. If it recommends an AI assistant for a Quality process, ask how it would demonstrate acceptable performance against the system's actual intended use, rather than a clean demo run on curated examples, and ask specifically how it tests missing evidence, conflicting source documents, retired content still sitting in the corpus, and a human reviewer disagreeing with the system's output. If it recommends a migration design, ask whether acceptance stops at record counts and technical reconciliation, or whether someone will actually test whether a migrated deviation record still carries the regulated meaning it had before the move, rather than just the same field values.

The sharpest single question at this stage is one most firms are never asked: can you say, before delivery starts, what evidence would tell us your recommendation shouldn't be accepted. A firm that can only describe what success looks like has built a description of the outcome it is being paid to produce, rather than an actual test. Push further and ask the firm to walk through a real past engagement where the evidence didn't support what the client wanted to hear, and what happened next. A firm with a genuine answer will have a specific story, including one where it said something the client didn't want to hear. A firm without one is telling you, indirectly, that its validation has never actually been allowed to fail.

An internal sign-off and an external scrutiny test sit at different evidence bars, and a validation package built for one rarely satisfies the other automatically. Ask the firm to show you the standard of evidence it expects to produce. A synthetic example, an internally generated artefact, or appropriately redacted evidence, none of it another client's confidential material, can demonstrate whether the firm's idea of proof actually connects a claim, an acceptance criterion, evidence, and the resulting decision, rather than stopping at a polished template.

The evidence this stage leaves behind is what's worth calling the acceptance basis: naming what will be tested, what would constitute failure, and who signs off on the result, built ahead of delivery rather than assembled afterward to match whatever got delivered.

Own: what remains once the firm's people are gone

The fourth test changes the nature of the conversation, because it asks about a moment the commercial relationship has already ended.

As a pre-signature test, this isn't about auditing a transition plan line by line. That execution question deserves its own treatment, which is what I've examined separately in what actually gets handed over once an implementation partner leaves. What belongs here, before signature, is narrower: does the firm even intend to leave the client capable of disagreeing with it.

The single most useful question here is one most buyers never think to ask a firm before signing anything: who inside our organisation will be capable of disagreeing with you six months after this engagement ends. An honest answer of nobody, because nobody will have the rationale, the evidence, or the authority to challenge a decision the firm made, means the firm has not designed for ownership to transfer at all, whatever its proposal promises about knowledge transfer.

Ask what it intends to leave behind, and listen for the difference between a list of deliverables (the configured objects, the workflows, the signed-off validation package) and a description of state: whether Quality could explain how its intent became system behaviour without calling the firm, whether Digital would understand which decisions can be changed safely and which reopen validation or security consequences, whether the organisation would retain a decision history detailed enough to brief the next supplier rather than starting from a blank page. Call the resulting document the ownership record: naming what stays internal, what stays with the firm's specialists, and who is accountable for each once the engagement ends. Ownership also includes knowing which conditions made the original decision valid in the first place, who monitors them, and what change should trigger a reassessment, since a decision that was right when it was made doesn't stay right by default.

External dependency is a legitimate choice a firm might design toward, worth judging on its own terms rather than penalising by default. Plenty of organisations rationally retain specialist Veeva or AI support rather than building every capability in-house, particularly smaller life sciences companies for whom that would be the wrong economic trade-off entirely. The distinction that actually matters, and the one worth testing before signature, is whether a prospective firm treats that dependency as something to be consciously designed and named, or as whatever shape the relationship happens to take because nobody decided otherwise before the engagement ended.

Where this sits relative to standard due diligence

A vendor-selection matrix, a reference check, and a certification review all still matter, and this framework sits alongside them rather than replacing any of them. They establish whether a firm belongs in the conversation at all: relevant experience, satisfied past clients, the right certifications, reasonable commercial terms.

What they were never built to test is how a firm reasons at the moment a regulated decision actually gets made or mismade, often quietly, well before anyone signs off on anything: when a local site's requirement conflicts with a global standard, when a validation result is inconvenient, when an AI system's output reads fluently but the source behind it is questionable. A generic procurement lens optimises for whether a firm can deliver the stated scope. Investigate, Architect, Prove, and Own optimise for whether the firm's reasoning, and the evidence behind it, will still hold up once nobody from the firm is in the room to explain it. A strong RFP score is evidence a firm can describe good practice. Applying that practice under pressure, when the convenient answer and the correct one diverge, is the separate skill this framework is built to surface. It also has a shelf life: the ownership record defined under Own is what tells you when to come back and check.

If you are already looking for an alternative

A related version of this question shows up after a firm is already engaged: something isn't working, and the question becomes what a genuine alternative would actually look like. The same four tests apply there too, in both directions. Applied backward, they diagnose what's actually missing in the current relationship: whether a validation package nobody outside the firm can defend traces back to a weak Prove test, or whether a configuration nobody can explain eighteen months on traces back to a weak Own test. Applied forward, to whatever alternative is being considered, whether that's a larger systems integrator, a boutique Veeva consultancy, an independent Veeva Vault consultant or architect, or an AI-focused advisory firm, the same four questions apply before signature, exactly as they would to a first hire. Switching firms without running that test on the replacement usually just moves the same quiet failure to a new supplier.

More on Veeva architecture and recovery engagements specifically →

Apply this to us before you apply it anywhere else

If you're comparing Navata with an independent Veeva Vault consultant, a boutique consultancy, a systems integrator, or an AI-focused advisory firm, apply exactly the same test to us. Ask us what we wanted to see before we would recommend anything. Ask us to walk through a past architecture decision we would still defend today, including the alternative we rejected. Ask what evidence would tell you our recommendation shouldn't be accepted, and ask for a real example of when a client's evidence didn't support what they wanted to hear. Ask who inside your organisation would be capable of disagreeing with us six months after we are gone. A case study written for a website answers none of those questions, and we wouldn't expect you to accept one as though it did.

The firms that can answer all four, specifically and with evidence rather than in outcomes, are worth a longer conversation regardless of which one you eventually choose. The ones that can't have told you something too, before you have signed anything committing you to find out the hard way.

More on how the Navata Method structures our own engagements: the Navata Method. For client-side Veeva architecture and recovery specifically: Veeva Vault Architecture.

Views expressed are personal and do not represent any employer or client.

About the author

Rohith Karanam Sreedhar

Founder & Principal

Rohith founded Navata after eighteen years building, validating and operating Veeva and quality systems inside global pharma, including leading Vault QMS architecture at Pfizer. He now helps regulated life-sciences teams find and own the architecture decisions already running inside their platforms.

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