AI PRODUCT COMPANY · HYDERABAD, INDIA

The work does itself.

SvaKrit AI builds agentic products that take an operation a company runs by hand and run it end to end — observing, deciding, acting, and accounting for what they did. Our own operations run the same way.

See the products How the platform works

┌───┐
│ │ │  svakrit ai
  └─┘  agent 04 · ok

$ svakrit agents status
04  site-safety        running  ok
07  gate-access        running  ok
11  engineering        paused   awaiting review
12  vendor-onboarding  running  ok

14:22:07  agent 04 — ppe violation, bay 2
          rule SITE-PPE-01 fired
          supervisor paged · evidence clipped
          logged [INC-4471]

14:24:31  agent 11 — refused: new dependency
          policy ENG-DEP-00 · reason recorded
          waiting on named reviewer

ILLUSTRATIVE · AGENT IDS, TIMES AND RULE NAMES ARE EXAMPLES

01 — THE LOOP

One stroke leaves an opening, runs the circuit, and turns back inward.

That is our mark, and it is also the specification. Every product closes the same four-step loop. Most tools stop after the second step and hand a person a dashboard. Most agents stop after the third and leave nobody able to explain what happened. The turn back inward is the fourth step, and it is what makes the first three safe to switch on.

Observe

It watches the real thing — a camera feed, a repository, a queue — continuously. Not on a schedule, and not only when someone remembers to look.

Decide

It judges what it saw against rules you wrote, in your words. Your thresholds, your exceptions, your escalation path. Not a vendor’s defaults.

Act

It does the thing, in your systems. Raises the incident, logs the vehicle, opens the pull request, pages the supervisor. Watching without acting is a nicer dashboard.

Account

Every decision, action and refusal written down and attributable, months later, to an auditor — not only live on a screen. This is the step everyone skips.

02 — THE PLATFORM UNDERNEATH

One runtime. Every operation.

Our products look nothing alike and share nearly everything: the same agent runtime, the same governance layer, the same audit trail, the same choice of where to run. The top and bottom layers stay yours — your sensors, your systems of record — and we do not ask you to move either.

That is why a third product is a question of months rather than years.

READ THE PLATFORM PAGE

LAYER 01 — YOURSCameras, repositories, queues, gates

LAYER 02 — OURSAgent runtime

plantoolsmemoryevaluation

LAYER 03 — OURSGovernance

policyrefusalshuman gatesaudit trail

LAYER 04 — YOURSIncident systems, pull requests, on-call

03 — PRODUCTS

ONE LIVE · ONE IN EARLY ACCESS

Two operations, taken over one at a time.

Each one is a loop we can close end to end. Neither needed the platform rebuilt for it, which is the whole point of having a platform.

PRODUCT 01

LIVE

CortexVigil

A vision AI platform — not a video-analytics application.

Describe what you want watched in plain English. The platform drafts the rule, checks it against the models actually deployed on that site, and simulates it over your own recorded footage before anything is enabled. Then it runs, on the cameras you already own.

Traffic, safety, attendance, security and process solutions get built on it — by us, by customers, and by partner agencies.

The platform Domains and traction

SolutionsLIVE

TRAFFICEDUCATIONSECURITYATTENDANCESAFETYRETAILLOGISTICS

MarketplaceNEXT PHASE

PUBLISH MODELS AND TEMPLATESREVENUE SHARE

StudioLIVE

SELF-SERVE AUTHORINGPLAIN ENGLISH TO VALIDATED RULESIMULATE ON YOUR OWN FOOTAGE

AI engineLIVE

DETECTIONTRACKINGFACE RECOGNITIONACTIVITY ANALYTICSMODEL REGISTRY

RuntimeLIVE

EDGE GPU OR CLOUDANY IP CAMERAOFFLINE-CAPABLEMULTI-TENANTMCP ACCESS

WHY A PLATFORM AND NOT A PRODUCT

Every vision AI use case is rebuilt from scratch today — attendance, PPE, traffic violation, classroom compliance each mean a separate vendor, model, integration and a three-to-six-month build. India has a very large installed camera base and no platform layer on top of it. The missing piece is not another application; it is the thing other applications get built on.

PRODUCT 02 · NAME PROVISIONAL

EARLY ACCESS

Engineering agents

The operation: routine engineering work.

Agents that pick up a ticket, plan the change, write it, test it and open the pull request — inside your repository, under your review rules. The plan and the merge each need a named human. Your engineers keep the merge button.

Read the product page

Plans before it edits

A written plan against the real codebase, approved or redirected before a line changes.

Lives in your repository

Your branches, your CI, your PR template. Nothing new for the team to log into.

Reports failure honestly

A flaky test is named as flaky, not retried until it passes.

Stays inside your walls

Self-hosted, with the model provider you have already approved.

It runs our own backlog first. A company whose thesis is that the work does itself should be the first place that is true — and it proves the architecture is not vision-specific.

WHERE THEY RUN TODAY

A multi-site workforce-attendance production pilot with LJS India, running attendance, loading and unloading, inventory and security on one platform.

A proof of concept on district traffic cameras in the Gujarat Police Innovation Challenge 2026 evaluation environment.

Product 02 is referred to in type until naming and the IP India search settle.

04 — HANDING OVER THE WHEEL

Nobody switches this on in one go.

An operation moves from your people to a SvaKrit product in three stages, and you can stop at any of them. Stage two is the right place to sit until the numbers convince you. There is no prize for leaving it early.

STAGE 01

Pick one operation

Not a department. One loop with a clear input and a clear outcome — one site, one repository, one queue. The rules it should follow are written down, in your words, before anything is deployed.

STAGE 02

Run it in shadow

It observes and decides, but every action waits for your person to approve it. You get a week of evidence about where it is right, where it is wrong, and which rule needs rewriting — before it touches anything.

STAGE 03

Give it the wheel, with limits

You choose which actions it may take alone and which still need a name against them. Those limits are enforced outside the model, and every one it hits is logged — including the ones it hits at 03:00.

05 — WHAT WE BUILD NEXT

A product company does not build whatever is asked for.

A new SvaKrit product has to clear three tests. If you run an operation that clears them, we would like to hear about it. That is how the first one started.

TEST 01

The operation is expensive to staff and boring to do.

TEST 02

Its mistakes are visible enough to measure.

TEST 03

The decision can be written down as a rule you would sign off on.

What does your team watch that a system should be watching?

Tell us the operation. We will say whether one of our products already covers it, whether it is a candidate for the next one, or whether you are better off with somebody else.

[hello@svakrit.ai] Book a walkthrough