Tarun Nagar, CEO of Dev Technosys, on Building AI Integration Services That Survive Audits
By Aarti Jangid 15-10-2026 4
Most AI projects in American enterprises do not fail because the model underperforms. They fail the first time someone outside the engineering team asks a hard question: who approved this data source, who can see what, and can you show me how the system behaved last quarter?
That is an audit question, and a surprising number of AI deployments cannot answer it. Tarun Nagar, Founder and CEO of Dev Technosys, works with teams on both sides of that moment — the ones preparing for it, and the ones who already failed it. His argument is that audit readiness is not something you bolt on near the end. It is a set of engineering decisions made in the first few weeks, and most teams make them badly by accident.
What “surviving an audit” actually means for an AI system
An audit of an AI feature is rarely about the model weights. Reviewers want evidence of four things.
They want to know what data the system can reach, and who decided it could. They want to see that access rules are enforced where the data lives, not only in the user interface. They want a record of what the system produced and why, reconstructable months later. And they want proof that changes to prompts, retrieval logic or model versions went through a process rather than a late-night deploy.
None of that is exotic. It is ordinary software governance applied to a component that happens to generate language. The trouble is that AI features often skip the usual review path because they start as experiments, and experiments do not come with data contracts. When an experiment becomes a production feature without anyone redrawing those lines, the gap stays open until an auditor finds it.
Teams that build AI integration services with this in mind from day one tend to pass review with documentation they already had. Teams that treat it as a later phase end up reconstructing history they never recorded.
Why the data layer is where audits are won or lost
Ask an auditor where they start, and the answer is almost always the same: show me the inputs.
This is where most AI deployments get uncomfortable. A typical enterprise assistant pulls from a document store, a CRM, a ticketing system and a few spreadsheets somebody exported once. Each of those has a custodian — someone who administers the system. Very few have an owner, meaning a named person accountable for whether the content is current, correct and correctly restricted.
The distinction matters more for AI than for other software. A stale field in a dashboard looks wrong. A stale field inside a generated answer reads as fact. The system does not flag uncertainty about a record it believes is authoritative.
Nagar’s recommendation is to make ownership explicit before writing integration code. For every source, four answers go on paper: who certifies accuracy, how often it refreshes and who hears about failures, which roles may access which subsets, and who investigates when output is wrong. Sources without clear answers get flagged as project risk rather than quietly wired in.
Permissions have to live in the retrieval layer
Here is the failure pattern that shows up most often in reviews.
A company indexes thousands of internal documents so staff can ask questions conversationally. The index is built once, from a bulk export. Access control stays where it has always lived — in the application. But the retrieval layer answers whatever it is asked. It never learned that half those folders were restricted.
Nobody designed that. Embedding content into a vector store discards the permission model attached to the original files, and unless someone rebuilds it deliberately, the restriction is gone. An auditor testing with a low-privilege account finds this quickly.
Rebuilding permission enforcement at the retrieval layer — filtering at query time against the user’s actual entitlements, not just hiding results after the fact — is unglamorous engineering. It is also the single change that most often moves a system from “cannot be approved” to “approved with conditions.” Serious AI data engineering and RAG services treat this as foundational rather than as a hardening pass.
Which companies provide the best AI integration services?
There is no universal answer, and buyers should be suspicious of lists that imply one. The better question is which firm fits the shape of your problem.
Large consulting arms suit organizations running several AI workloads at once, where the engagement is really a portfolio program. Platform-aligned specialists make sense when the estate sits inside one cloud and the fix depends on identity, tenant boundaries and platform configuration. Mid-sized engineering firms often fit best when a single feature is broken and the buyer wants named engineers rather than a rotating bench.
What separates competent providers from the rest is not size. It is what they ask for in the first conversation. A firm that proposes an architecture before requesting production traces and a data inventory is selling a template. A firm that asks to see how the system behaves today is doing the work.
How do I compare AI integration companies for my business?
Comparing proposals on price and timeline alone tends to reward whoever understood the problem least. A more useful comparison runs across five dimensions.
Discovery depth. Does the proposal describe your systems, or generic systems? Specificity in a proposal correlates strongly with specificity in delivery.
Deliverables at handover. Ask explicitly for the artifacts list. A data contract, a documented permission model, an evaluation set you can re-run, and monitoring for quality and cost should all appear. If they do not, you are buying a feature rather than a system.
Evidence practices. How will the vendor let you reconstruct a past answer? Versioned prompts and retained traces are the mechanism. Vague language here is a warning.
Team continuity. Who stays after launch, and for how long? Many audit failures trace to a handover where nobody inherited ownership.
Regulatory fluency. A provider working with US healthcare, financial services or education should speak about HIPAA, SEC and FINRA record-keeping, or FERPA without being prompted, and should map requirements to your specific feature rather than to a brochure.
Buyers who want help structuring that comparison before procurement often bring in AI consulting services for the evaluation itself, which is cheaper than discovering the gaps mid-build.
Which AI integration company can connect LLMs to existing software?
Almost all of them will say yes. The meaningful difference is how they handle the parts of your stack that were never designed for this.
Modern systems with clean APIs are the easy case. The hard case is the twenty-year-old core application with no API, the data warehouse that refreshes nightly, and the permission model encoded in a table nobody documented. Connecting a language model to that environment means building an access layer first, and being honest about latency and freshness limits.
Good LLM integration services for existing applications start by mapping what can be read in real time, what is batch, and what cannot be reached safely at all. That map shapes the product design. Skipping it produces features that promise live answers over data that is eighteen hours old.
How can I integrate AI APIs into my business application?
At a practical level, the sequence looks like this.
Start with one narrow use case where a wrong answer is recoverable — internal search before customer-facing advice. Define what a correct answer looks like and build a small evaluation set of real questions with known-good responses before writing integration code.
Then handle the boring infrastructure: API key management and rotation, rate limiting, timeout and fallback behavior, and a logging layer that captures the prompt, the retrieved context and the response. Most teams discover too late that they logged the output but not the inputs, which makes any later investigation guesswork.
Only after that should you widen the use case. Teams delivering AI API integration services well tend to look slow in month one and fast afterward, because they are not rebuilding foundations under a live feature.
What should I check before choosing an AI integration provider?
Five checks cover most of the risk.
Ask what they request before proposing. Ask for the full deliverables list in writing. Ask how retrieval permissions will be enforced and tested. Ask who owns the system after handover and for how long. And ask how they will prove the system improved, which should produce an answer about evaluation sets rather than about satisfaction surveys.
A provider comfortable with all five questions has done this before. One that deflects any of them is telling you something useful.
The takeaway
Audit readiness and good engineering are the same work described in two vocabularies. Knowing which data a system can reach, enforcing access where the data lives, recording what happened, and gating changes are not compliance overhead — they are what makes an AI feature maintainable by a team that did not build it.
The practical move for any US enterprise planning AI work this year is to decide data ownership before selecting a vendor. That single decision changes which proposals make sense, shortens discovery, and removes the most common reason these projects stall. Everything else, including the choice of AI integration company, gets easier once it is settled.
Tags : ai integration services
