Sarvaswa AI Labs
Enterprise AI

Enterprise AI that survives the security review.

In most large organisations the difficulty with artificial intelligence has not been finding a use case, and it has rarely been the capability of the models themselves. The difficulty is that a promising pilot meets data residency rules, an access control model built over a decade, a compliance function that needs an audit trail, and three systems from 2011 that hold the data everything depends on. We build enterprise AI that is designed around those constraints from the first architecture conversation rather than colliding with them in month four, running inside your own cloud, governed by the controls you already operate, and owned by you at handover.

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Inside your cloud · SOC 2, HIPAA and regional requirements scoped upfront

Why pilots stall

The pilot succeeds, and then the real system meets the organisation.

The pattern repeats with remarkable consistency across large organisations. A small team builds something genuinely impressive in a few weeks, usually against exported sample data on a laptop or in a sandbox account, and the demonstration goes well enough that the project gets funded properly. The trouble begins at exactly the point where the system has to touch production data, because that is when the pilot stops being a technical exercise and becomes an organisational one, and almost nothing about the prototype prepared anybody for that conversation.

What surfaces at that stage is a set of questions the prototype never had to answer. Where does the data physically reside while the model processes it, and does that satisfy the jurisdiction your regulator cares about. Which role can see which records, and does the system honour the entitlements your identity provider already enforces or does it quietly bypass them. When somebody asks in eighteen months why a particular decision was made, can you reconstruct it. And if the answer to any of those is uncomfortable, the project does not usually fail loudly, it simply stops moving while everybody waits for somebody else to take the risk.

Our view is that these questions are architectural rather than administrative, which means they cannot be resolved by adding a policy document to a system that was designed without them. A system that sends your data outside your perimeter cannot be made compliant by promising not to look at it, and a system that maintains its own separate permission model cannot be made safe by documenting who is supposed to have access. Designing for the constraints from the beginning costs slightly more in week two and saves the entire project in month four, which is the trade we consistently recommend.

What we design around

Six constraints that decide whether this reaches production.

These are the six subjects that come up in every enterprise engagement we run, usually in the order below, and the reason they are worth reading now is that each one is considerably cheaper to solve at the architecture stage than after a system exists and has to be rebuilt around them.

Data residency and perimeter

The first question your security team will ask is where the data goes, and for regulated work the only comfortable answer is that it does not go anywhere. Sending records to an external inference endpoint creates a boundary crossing that has to be justified, documented, and defended at renewal time, and in several jurisdictions it simply cannot be justified at all for certain categories of data.

How we handle it

We run inference inside your own cloud account so the data never leaves the boundary your organisation already defends.

Compliance and audit evidence

A control framework such as SOC 2 or HIPAA does not ask whether your system is careful, it asks whether you can produce evidence, and evidence has to be designed in rather than reconstructed later. The same applies to sector-specific regimes, where the requirement is usually not that a human made every decision but that any decision can be explained afterwards with the inputs that produced it.

How we handle it

Every run is traced with what executed, on which data, and with what result, so the audit trail is a by-product of normal operation.

Identity and access control

Large organisations have spent years building entitlement models that determine who may see which records, and an AI system that maintains a second, separate notion of permissions is a genuine security problem regardless of how carefully it is configured. The failure mode is subtle and serious: a retrieval layer that ignores row-level access will happily summarise a document the person asking was never entitled to open.

How we handle it

Access follows your existing identity provider and entitlements, so the system can never show someone more than they could already see.

Legacy system integration

The data that matters most is frequently held by the systems that are hardest to reach, whether that is a mainframe, a decade-old ERP, a scheduled file drop, or an internal service whose original authors left years ago. Any proposal that quietly assumes clean APIs everywhere is either underestimating the work or planning to solve an easier problem than the one you actually have.

How we handle it

We integrate with what exists, including file-based and batch interfaces, rather than requiring a modernisation programme first.

Governance and model oversight

Once a model influences decisions, somebody has to own the question of whether it is still behaving as intended, because model behaviour drifts as the underlying business changes even when nobody touches the code. Enterprises increasingly need to demonstrate this oversight to a risk committee, and demonstrating it requires measurements that were being collected all along.

How we handle it

Evaluation harnesses and continuous monitoring are built in, so drift is measured rather than noticed by a customer.

Vendor lock-in

The most expensive decision in an enterprise AI programme is often the one nobody examines closely, which is how much of the resulting capability lives with the vendor rather than with you. A system that depends on a hosted layer you do not control becomes a permanent line item and a permanent negotiating weakness, and unwinding it later is considerably harder than avoiding it at the start.

How we handle it

You own the source code, the models, the pipelines, and the runbooks, and nothing keeps running only because we are still here.

Where it lands first

Five places enterprise AI reaches production soonest.

The programmes that reach production tend to begin in the same handful of places, not because these are the most ambitious applications but because each one has a clear owner, a measurable result, and a workable answer to the constraints above. Ambition is easier to fund once something is already running.

Document intelligence over sensitive corpora

Contracts, clinical records, compliance filings, and policy documents usually hold the organisation's most valuable unstructured information and are precisely the material that could not previously be exposed to an AI system. Handling this properly means governing the data before the model sees it, which is why the ingestion layer matters more here than the model does.

Best where knowledge exists but cannot be searched without breaching a control

Analytics access for people who do not write SQL

In most enterprises the reporting queue is a genuine constraint on decision speed, because business users with a straightforward question wait days for analyst capacity while the answer already exists in the warehouse. Making that data answerable in plain language removes the queue without removing the governance, since the same access rules still apply to every query.

Best where the data is already consolidated and the bottleneck is human capacity

Operational automation in the back office

Document intake, queue triage, reconciliation, and exception handling are high-volume, rule-adjacent work where a mechanical check can confirm the result, which makes them unusually safe places to introduce automation. The business case is also the easiest to defend, because the current cost is already measured in hours somebody is visibly spending.

Best where volume is high and correctness can be checked against a system of record

Multi-step agent workflows

Processes that require several kinds of expertise and several systems, such as claims triage or regulatory filing preparation, are where agent architectures earn their additional complexity. These arrive later in a programme by design, once the organisation has evidence from simpler systems and a governance model it trusts.

Best once earlier systems have established trust and the guardrails are proven

Domain models trained on proprietary data

Where terminology, decision criteria, and institutional reasoning genuinely differ from the public internet, a model trained on your own material behaves in a way no general model reliably matches. This also removes an external dependency entirely, since the resulting weights live in your infrastructure and answer only to you.

Best where domain language is specialised and the corpus cannot leave the perimeter

If your data already lives on a modern platform, the delivery detail is covered separately. Teams on Databricks should read Databricks Intelligence, teams on Snowflake should read Snowflake Intelligence, and anyone weighing a model trained on their own corpus will find the trade-offs set out in the fine-tuning explainer.

The architecture decision

Bring the AI to your data, rather than your data to the AI.

Nearly every meaningful decision in an enterprise AI programme follows from one architectural choice, which is whether the data travels to the intelligence or the intelligence is deployed next to the data. The first arrangement is faster to demonstrate and is how most pilots are built, since calling a hosted endpoint requires very little setup. The second is what survives a security review, because when inference happens inside the account you already control, a whole category of questions about boundaries, retention, and jurisdiction stops applying rather than needing to be answered.

This is not an argument against commercial models, and we use them constantly where the deployment sits inside your perimeter, whether that is Claude through a cloud provider under your own account, an open model on infrastructure you own, or a combination chosen per workload. The distinction that matters is not which model you use but where it runs and who holds the keys, and framing it that way tends to make the conversation with your security function considerably shorter.

We have built this way for regulated work before. In one engagement for an enterprise with a high-throughput workload, the requirement was that sensitive data could not reach a commercial API at all, so we trained a small language model on their proprietary data and deployed it on GPU infrastructure in their own account, then added predictive autoscaling that forecasts load and provisions ahead of demand. All inference stayed inside their infrastructure, monthly compute cost fell by roughly forty percent against the previous arrangement, and their team runs the system today.

How we work

Designed for the way enterprise decisions are actually made.

Enterprise delivery differs from startup delivery mainly in who has to agree, and a plan that ignores that reality tends to stall between phases. The sequence below is arranged so that the people who can stop the project are involved before the work that would be wasted, rather than afterwards.

  1. Discovery and constraint mapping

    Over one to two weeks we work through the use case alongside the constraints, which means your security architect and a compliance representative are in the room early rather than reviewing a finished design. The output is an architecture, a costing, and a written account of how each constraint is satisfied, in a form your internal approvers can actually assess.

  2. Security review, before the build

    We take the architecture through your review process while it is still inexpensive to change, because a boundary or entitlement objection raised at this point costs a conversation and the same objection raised after implementation costs a rebuild. In our experience this step also converts sceptical reviewers into useful collaborators, since they are being consulted rather than presented with a decision.

  3. One system, in production

    We build a single well-chosen system properly rather than several in parallel, deploy it inside your environment, and put it in front of real users with the governance already switched on. A working system that satisfies your controls does more to unlock the next phase of funding than any number of successful pilots, because it settles the question of whether this can be done here.

  4. Extend, with the pattern established

    Subsequent systems reuse the ingestion, governance, and observability foundations built for the first, which is why the second and third deployments are consistently faster than the first. Most individual systems reach production in eight to fourteen weeks, and larger platform builds such as a full lakehouse programme typically run twelve to sixteen.

  5. Transfer and step back

    Your engineers work alongside ours throughout rather than receiving documentation at the end, and by completion they hold the source code, the models, the pipelines, the evaluation harnesses, and the runbooks. Our involvement is designed to decrease, and we remain available afterwards because you want continuity rather than because the system cannot run without us.

What you keep

A capability inside the organisation, not a dependency on ours.

Everything we build runs inside your cloud and within your data boundaries, and at completion you hold the source code, any model weights and training scripts where fine-tuning was involved, the data pipelines, the evaluation harnesses, the infrastructure definitions, and the operational runbooks. There is no hosted layer of ours quietly sitting in the middle of your architecture, which means the question of what happens if the relationship ends has a straightforward answer: the system carries on, and your engineers maintain it.

For an enterprise this matters beyond the commercial arithmetic, because a capability your own people can read, modify, and extend compounds over the years while a rented one merely renews. It also changes the negotiation, since a team that could operate the system without a vendor is in a materially different position from one that could not. That is the outcome we build towards, and it is the reason knowledge transfer is structured into the engagement rather than promised at the end of it.

Questions worth answering

Enterprise AI, answered.

The models are usually the same, and almost everything around them is different. Enterprise work has to satisfy data residency rules, honour an access control model built over many years, produce audit evidence for a compliance function, integrate with systems that predate modern APIs, and survive a security review conducted by people whose job is to find problems. A prototype that ignores those requirements can be built in a fortnight and will not reach production, which is why we design around the constraints from the first architecture conversation rather than treating them as a later approval step.
It does not leave. We deploy inference inside your own cloud account, which means records are processed within the boundary your organisation already defends and governs, and a whole category of questions about retention, jurisdiction, and third-party access stops applying rather than needing to be answered. Where a commercial model is the right choice we run it through a deployment under your own account rather than a shared external endpoint, and where residency requirements are strict we use open models on infrastructure you control.
These are scoped at the beginning of the engagement with your security and compliance people in the room, rather than discovered during a review after the design is fixed. Practically that means the architecture is documented against each control, access follows your existing identity provider and entitlements rather than a separate permission model, personal data is detected and masked at ingestion where the workflow requires it, and every run is traced with what executed, on which data, and with what result. The audit trail is a by-product of normal operation, which is what makes evidence gathering straightforward later.
Yes, and it usually has to, because the data that matters most is frequently held in the systems that are hardest to reach, including mainframes, long-established ERP platforms, scheduled file drops, and internal services whose original authors have long since moved on. We integrate with what actually exists, including batch and file-based interfaces, rather than requiring a modernisation programme as a precondition. Any proposal that assumes clean APIs across an enterprise estate is solving an easier problem than the one you have.
Discovery and constraint mapping run one to two weeks and produce an architecture, a costing, and a written account of how each constraint is satisfied in a form your approvers can assess. Most individual systems then reach production in eight to fourteen weeks, while larger platform programmes such as a full lakehouse build typically run twelve to sixteen. Second and third systems are consistently faster than the first, because they reuse the ingestion, governance, and observability foundations already established.
In the great majority of cases the pilot was built against exported sample data in a sandbox, and everything that made it easy to build is exactly what made it impossible to deploy. The system had no answer for where production data would be processed, no way to honour existing entitlements, no audit trail, and no route into the legacy systems holding the real records. None of those are model problems and none are solved by a better prompt, which is why we bring the security and compliance conversation forward into architecture rather than leaving it as an approval gate at the end.
No, and we deliberately stay tech-agnostic because the right answer depends on constraints that vary between organisations and sometimes between workloads inside one organisation. We work with Claude and other commercial models deployed under your own account, with open models such as Llama or Mistral where cost, latency, or residency requirements point that way, and across AWS, Azure, GCP, Databricks, and Snowflake. The choice follows your constraints rather than a partnership we are paid to favour, and a single programme frequently uses more than one.
The system keeps running and your engineers maintain it, which is the outcome the engagement is structured to produce. You own the source code, any model weights and training scripts, the data pipelines, the evaluation harnesses, the infrastructure definitions, and the runbooks, all deployed inside your own cloud with no hosted layer of ours in the middle. Your team builds alongside ours throughout rather than receiving a handover document at the end, so the knowledge to operate and extend the system is genuinely inside your organisation before we step back.

Bring your constraints, and we will tell you what is buildable inside them.

Fifteen minutes is enough to work through where your data has to stay, which controls apply, and what the systems holding your records will realistically allow. If the honest answer is that something needs to change before AI is worth attempting, you will hear that rather than a proposal.

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