Sarvaswa AI Labs

AI automation services for the work that quietly eats your week.

Every operation carries a layer of work that nobody planned for and nobody enjoys: reading documents that arrive in slightly different formats, deciding which queue something belongs in, chasing the mismatch between two systems that were supposed to agree, and rebuilding the same report every Monday morning. We design and build AI automation that absorbs that layer, with a human approving anything that matters, so your team spends its attention on the decisions that actually require judgment.

Book a callSee where it pays

Proven on your real cases before it goes live, and run by your own team from the day we hand it over.

How it stays safe

A model does the reading. A check and a person decide whether it counts.

  1. 01

    Work arrives

    Documents, tickets, files, any layout

  2. 02

    Model interprets

    Extracts, classifies, or drafts

  3. 03

    Mechanical check

    Compared to your system of record

  4. 04

    Human approval

    Before anything irreversible

  5. 05

    Action and trace

    Posted, routed, and logged

  6. Fails the check? It goes to a person with the reasoning attached. Nothing passes silently.

    Every run leaves a trace you can reconstruct afterwards, which is usually what makes the project possible at all in regulated work.

What changed

Older automation broke on the exceptions. This does not.

Most teams have already tried to automate this work at least once, usually with rule-based tooling or robotic process automation, and the experience tends to follow the same arc. The pilot goes well because the sample documents were consistent, and then reality arrives in the form of a supplier who reformats their invoice, a broker who sends a scanned PDF instead of a structured file, or a field that is sometimes blank and sometimes contains a note explaining why it is blank.

Rule-based automation and RPA

Follows instructions written in advance.

  • A rule exists for every case someone anticipated, and none for the cases they did not
  • A supplier changing their invoice layout becomes an incident
  • Fails loudly and predictably, which is its one real advantage
  • The exception queue grows until somebody is doing the original job again, plus maintaining the bot

AI automation

Interprets the variation directly.

  • Reads an invoice the way a person does, without a template for where the total sits
  • The same capability covers classification, extraction, summarisation, and the small judgments between two systems
  • Can be confidently wrong and entirely plausible, which is harder to notice at scale
  • So every system pairs it with a mechanical check, a human approval gate, and a trace of every run

The automation is the easy part. The checking is what makes it safe to rely on, and it is where most of the engineering on this page actually lives.

Where it pays

Six shapes of work worth automating.

These categories come up in almost every operation we look at, regardless of the industry, because the underlying shape of the work is the same whether the documents are insurance claims, supplier invoices, or clinical records. Reading through them is usually the fastest way to recognise your own backlog.

Document-heavy intake

Work that begins when a document arrives and cannot proceed until somebody has read it, understood it, and moved the relevant information into a system. This is normally the highest-value place to start, because the volume is predictable, the cost per document is easy to measure, and success can be checked by comparing extracted fields against records you already hold.

For example

  • Reading supplier invoices in whatever format each supplier prefers, matching them to purchase orders, and posting them for approval
  • Extracting structured data from claims, contracts, or onboarding packs that arrive as scans, emails, and portal uploads
Document intelligence for regulated data

Triage and routing

Any queue where the first action is a person deciding what something is and where it should go. Classification of this kind is repetitive, requires context rather than deep judgment, and consumes disproportionate amounts of senior attention, since the people best at triage are usually the people you would rather have doing something else.

For example

  • Sorting inbound support tickets by root cause, urgency, and owning team, then drafting a first response for review
  • Assessing incoming claims or applications against policy so that straightforward cases move immediately and complex ones reach a specialist
How triage agents are built and kept safe

Reconciliation and exception handling

The daily comparison between two systems that ought to agree and frequently do not, followed by the slow work of explaining each difference. Most exceptions turn out to belong to a handful of recurring causes such as rounding conventions, timezone cutoffs, or duplicate events, which makes this work unusually well suited to automation that learns the patterns and handles them consistently.

For example

  • Matching settlement files against an internal ledger and resolving the recurring break classes automatically
  • Comparing inventory or shipment records across systems and investigating each discrepancy with the supporting evidence attached

Reporting and analysis

Recurring reports that somebody assembles by hand from several sources, and the follow-up questions that arrive the moment the report is circulated. Automating the assembly is straightforward, and the larger gain usually comes from letting people ask the follow-up questions directly rather than waiting for a second version of the report.

For example

  • Producing the weekly performance summary across channels, with the notable changes explained rather than merely listed
  • Answering plain-language questions against your warehouse so that routine analysis does not require a ticket to the data team
A shipped example: the Slack data copilot

Data plumbing that keeps breaking

The integrations that quietly stop working when a partner changes a format, a firmware update alters a payload, or a schema shifts upstream. The failure is rarely noticed on the day it happens, which is what makes it expensive, and this category is where automation that monitors and repairs itself pays for itself most clearly.

For example

  • Detecting when a partner feed stops parsing, diagnosing the change, and drafting the fix with tests against historical traffic
  • Watching pipeline failure queues and repairing the recurring causes before the dashboard gap is noticed downstream
Loops that repair pipelines unsupervised

Customer-facing operations

Work where the person waiting is a customer, which raises the stakes and narrows what should be automated, since a wrong answer here costs more than internal rework. We build these carefully, with tool access so the interaction ends in a completed action rather than a promise, and with clear boundaries around anything involving money, entitlements, or complaints.

For example

  • Handling routine account changes end to end, including the system update and a reference the customer can quote back
  • Answering front-desk calls in natural conversation, booking the appointment, and updating the record during the call
Assistants that complete the request

What to automate first

Four tests that predict whether automation will hold.

We apply these four tests to every candidate workflow during discovery, and we apply them before anybody writes code, because a workflow that fails two of them will consume a budget and return very little regardless of how good the underlying model is.

The work comes back regularly

Automation spreads its build cost across every future run, which means the arithmetic only works when the work genuinely recurs. If a task appears a handful of times a year, the honest recommendation is usually a better template or a well-aimed prompt in a tool you already own, and we would rather tell you that than build something that never earns back its cost.

Something can check the result

There has to be a way to reject a wrong answer without a person reading every single output, whether that is comparing extracted fields against a system of record, replaying against historical cases with known outcomes, or validating against a schema. Where correctness is a matter of professional opinion rather than something a check can settle, automation multiplies the volume of opinions instead of reducing the workload.

The inputs are reachable

The automation needs actual access to the documents, systems, and history involved, through an API, a database, a mailbox, or a file location, and it needs that access under permissions your security team is willing to grant. Work that depends on information living only in somebody's head or in a system nobody can integrate with is not ready yet, though it often becomes ready after a small amount of groundwork.

Someone owns the outcome

A named person has to care whether the automation is working, review what it escalates, and decide when the rules should change. Automations that belong to nobody drift quietly out of usefulness, and the failure is almost never technical. We ask who that person is during scoping, because their involvement matters more to the result than most architectural decisions.

Have a workflow in mind?

Score it against the four tests in fifteen minutes.

Bring the queue, the report, or the reconciliation that eats the most time. We will tell you which tests it passes, roughly what it would cost to run at your volume, and whether the honest answer is a fixed integration rather than AI.

Book a callSee the three levels

How far to go

Three levels, and most teams should start at the first.

Automation is not a single decision but a ladder, and the sensible approach is to climb it one rung at a time as your team accumulates evidence that the previous rung behaves well. Moving straight to the top is the most common way these projects lose the trust they depend on.

Assisted, where a person stays in the loop

The automation does the reading, the drafting, and the preparation, and a person reviews and approves before anything is committed. Value arrives immediately because the slow part of the work was never the clicking, it was the reading, and your team keeps complete control while they build an accurate sense of how often the system is right.

Human role

A person approves every output before it counts

Supervised, where only exceptions reach a person

Once the evidence shows the automation handles the common cases reliably, the straightforward cases begin flowing through on their own while anything unusual, ambiguous, or high-value is escalated with the reasoning attached. This is where the workload genuinely shifts, because your team moves from processing everything to handling only what actually needed them.

Human role

A person handles exceptions and spot-checks the rest

Unsupervised, where the system runs on its own

The automation runs on a schedule or in response to events with nobody watching, which is only responsible when a mechanical check can genuinely fail bad work and hard limits stop a runaway. This is the discipline we call loop engineering, and it suits recurring technical work with objective verification far better than it suits anything involving customers or money.

Human role

A person reviews what the system escalates, and merges

The two pages behind this one go considerably deeper. If the work you have in mind involves software that reasons and calls your tools directly, read AI agent development, which covers how those systems are built and kept safe. If you are looking at recurring technical work that could run without anybody watching, read loop engineering, which explains the four conditions that decide whether unsupervised automation is worth building at all.

What changes for your team

The work changes shape before it changes size.

The people who currently perform the work should be involved in designing its replacement, and not simply informed once the system is ready. They know which cases are genuinely difficult, which shortcuts are safe, and which apparently minor details would cause a serious problem if handled incorrectly. That knowledge normally exists nowhere in writing, and capturing it is one of the more valuable things a discovery phase produces, quite apart from the automation itself.

The change worth planning for is a new responsibility rather than a redundancy: somebody now owns a system that makes decisions, which means reviewing what it escalates, noticing when its behaviour drifts, and deciding when the boundaries should move. We build the runbooks and the observability for exactly that role, and we would rather your team grows into it during the engagement than discovers it after we have gone.

How we work

Find the work, prove it by hand, then automate it.

The order matters more than it appears to, because a process that was never reliable when performed carefully by a person does not become reliable by being scheduled. Each step below produces something you can act on even if you decide not to continue.

  1. Discovery, over one to two weeks

    We sit with the people doing the work and map what actually happens rather than what the process documentation claims happens, because the difference between those two is usually where the automation opportunity lives. You receive a ranked list of candidate workflows scored against the four tests, a cost model at your real volumes, and a clear statement of which candidates we would advise against.

  2. One workflow, proven by hand

    Before anything is scheduled, we run the full cycle manually against real cases to establish what good output looks like and how often it is achievable. This step surfaces the awkward exceptions early, while they are still cheap to design around, and it produces the labelled examples that the verification layer will later depend on.

  3. Build with the checks first

    The verification layer is built alongside the automation rather than added to it, since the check is what determines whether the output can be trusted and therefore what determines the entire design. Permissions start read-only, approval gates are placed where the stakes require them, and every run is traced so that surprising behaviour can be reconstructed rather than guessed at.

  4. Run assisted, then widen

    The automation goes live with a person approving each output, which builds both the evidence and the confidence needed to move further up the ladder. As the numbers justify it, straightforward cases begin flowing through automatically while exceptions continue to reach a human, and the boundary moves deliberately rather than by default.

  5. Hand over and step back

    Your team receives the repository, the prompts and skill files, the verification suites, the infrastructure definitions, and the runbook they will operate it with. Most engagements reach production between eight and fourteen weeks from kickoff, and our involvement decreases by design as your own people take the system over.

Scope, honestly

What we will tell you not to automate.

We keep automation away from decisions where being correct is a matter of professional judgment rather than something a check can settle, which in practice covers clinical decisions, legal advice, hiring outcomes, credit and coverage determinations, and anything that moves money irreversibly. The systems can produce an answer in all of those situations, and the difficulty is that nobody can reliably tell afterwards whether the answer was right, so what you gain in speed you lose several times over in risk.

We are equally direct when the honest answer is that you do not need AI at all. A meaningful share of the workflows we are asked to look at turn out to be better solved by fixing an integration between two systems that never spoke properly, redesigning a form so the data arrives clean in the first place, or removing a step that exists only because it existed last year. Those recommendations cost you less and last longer, and we would rather give them in the first fortnight than build something impressive that quietly costs more than the work it replaced.

If your constraints are the harder part of the conversation, data residency, audit evidence, or entitlements, start with Enterprise AI, which covers how we design around them before the build. If you would rather build the automation with your own team, an embedded Forward Deployed Engineer does exactly this work inside your repos.

Shipped

What this looks like in production.

Marketing AI with humans in the loopUS marketing agency

An end-to-end AI marketing engine with humans in the loop, built for Robomarketer

A marketing-operations engine for a US agency: it writes, designs, builds campaigns, and reports automatically, and every decision that matters waits for a person to approve or reject it in Slack. The same shape as the pipeline above, running every day.

Stack

AWSCommercial LLM integrationsCustom orchestration layerSlack HITL workflowPredictive reporting
See all use cases

E2E

From creative to reporting

HITL

Slack approve/reject gate

24/7

Competitor research running

AI automation, answered.

Rule-based automation and robotic process automation follow instructions that somebody wrote in advance, which works well while the inputs stay consistent and breaks the moment they vary, because a rule exists for every case that was anticipated and no case that was not. AI automation uses models that can interpret variation directly, so an invoice arriving in an unfamiliar layout or a message phrased in an unusual way does not require a new rule. The trade-off is that a model can be confidently wrong in a way a rule cannot, which is why every system we build pairs the automation with a mechanical check, a human approval gate for anything irreversible, and complete tracing of each run.
Start with work that recurs regularly, produces output something can objectively check, draws on systems the automation can genuinely reach, and has a named owner who cares whether it is working. In practice that usually points to document-heavy intake, triage and routing, or reconciliation, because those three combine high volume with a clear definition of a correct result. During discovery we score your candidate workflows against those four tests and give you a ranked list, including the candidates we would advise against and the reasons why.
Discovery runs one to two weeks and produces a ranked list of workflows, a cost model at your real volumes, and an honest read on what should not be automated. Most engagements then reach production between eight and fourteen weeks from kickoff. We deliberately begin with a single workflow running in assisted mode, where a person approves each output, because that produces value quickly while building the evidence needed before anything runs with less supervision.
Accuracy depends far more on the workflow and the verification design than on the model, which is why we measure it against your real cases during the manual phase rather than quoting a general figure that would not mean anything for your situation. The more useful question is what happens when it is wrong, and the answer is structural: a mechanical check compares the output against a system of record or a set of known cases before it counts, anything the check cannot settle is escalated to a person with the reasoning attached, and nothing irreversible proceeds without human approval. Every run is traced, so a wrong result can be reconstructed and corrected rather than merely noticed.
In the engagements we run, the immediate effect is almost never that fewer people are needed. What changes is that the same people stop spending their day on the mechanical portion of the work and start spending it on the portion that required their expertise, which is usually why they were hired. A new responsibility does appear, because somebody has to own a system that makes decisions, review what it escalates, and notice when its behaviour drifts. We build the runbooks and observability for that role deliberately, and we recommend involving the people who currently do the work in designing its replacement, since they hold the knowledge about difficult cases that exists nowhere in writing.
Everything runs inside your own cloud and within your existing data boundaries, using the access controls your security team already operates rather than a parallel set of permissions. SOC 2, HIPAA, and regional data residency requirements are scoped at the beginning of the engagement rather than discovered during a review, permissions begin read-only and each write is justified and re-audited, and every action is traced with a record of what ran, on what data, and with what result. For regulated work this audit trail is frequently the reason a project can proceed at all.
There are two costs and we model both before you commit. The build is scoped after the one to two week discovery, once we know which workflow is involved and which systems it touches. The running cost depends on volume, on how much context each operation requires, and on which model handles each step, so we select models per step rather than defaulting to the largest one and we instrument spending from the first week. The number we build towards is the cost per accepted result rather than raw usage, because automation that produces output your team rejects half the time is not saving anything regardless of how little it costs to run.
The repository, the prompts and skill files, the verification suites, the infrastructure definitions and the runbook your team operates the system with. Nothing continues to depend on us afterwards, which means your engineers can change how the automation behaves as your process changes, and that matters more than it may sound, because processes always change and an automation you cannot modify becomes an obstacle within a year.

Tell us where the week goes, and we will tell you what is worth automating.

Fifteen minutes is usually enough to recognise which parts of your operation pass the four tests and which do not. If the honest answer turns out to be a fixed integration or a better form rather than AI, you will hear that, and the assessment costs you nothing.

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