Shivya Technologies
AI agents you can trust with a real decision.
Two capabilities that sit on top of whatever agent stack you already run: a gate that verifies an output before it becomes an action, and a world model that lets you simulate a plan before you commit to it.
The problem
An agent's output isn't the same as a decision you can trust.
Companies now have AI agents that write filings, memos, claims, and reports. But every one of those outputs is something somebody still has to trust — so a human checks everything by hand, and the promised time savings never show up. Worse: nobody finds out whether the agent was right until the customs office, the payer, or the regulator responds, days later — and by then, nothing gets learned from the answer.
Gap one
Nothing stops a wrong output before it becomes an action.
Gap two
Nothing connects what happened afterward back to the decision that caused it.
"Here are the 14 filings from last week that would have been blocked. Eleven of them were genuinely wrong."
Capability one
Verify before it acts. Learn after the world responds.
Think of it as a touchstone — the stone you rub gold against to see whether it's real. Nothing crosses the line until it's been tested against something hard. A gate checks every agent output before it turns into an action: it passes, blocks, or escalates to a human, with the exact failing values and the source page highlighted. Days or weeks later, a loop picks up what actually happened — cleared, queried, rejected, charged back, audited — and feeds it back so the system measurably improves.
Output
Deterministic Checks
Escalate
↻ Loop: the real-world outcome — cleared, queried, rejected, charged back — feeds back into the Gate days or weeks later, so the checks get sharper over time.
The AI proposes. It never judges. The checks themselves are deterministic — that's what makes an output defensible in front of an auditor or a regulator.
Checked at the boundary
Every check runs at the action boundary — before anything happens downstream, not in a review meeting after the fact.
Evidence, not vibes
Every claim points at the exact source page and line it came from.
Rules pinned to a date
Versioned per domain, so you're checked against the rule that applied then — not today's.
Escalation with an SLA
A human approve/override screen, not an open-ended queue nobody owns.
Signed audit record
A tamper-evident record per action, built for the moment someone asks "why did this happen."
Outcome tracking
A dashboard on false-positive rate and outcome rate — not vanity metrics.
Capability two
Model the world. Test the process before it happens.
Instead of writing more prompt chains, you declare the world once: the entities, the states they can be in, what moves are legal, what must never be true, and how long things actually take. Agents then act inside that world — and you can rehearse. One model gives you a validator, a planner, and a simulator. The pitch isn't "AI agents" — it's that you can test your process before you run it.
Entities · States · Legal moves · Hard rules · Timing
Rehearse before you commit — the same declared world powers all three.
Author the domain once
Entities, states, legal transitions, and hard rules — declared once, not re-derived in every prompt.
Cheapest legal path
A planner that finds it within a budget — not just any path that technically works.
Thousands of what-ifs
A simulator that runs at scale, using realistic timings — not guesses.
Auto-generated edge cases
Delays, missing documents, difficult counterparties — generated, not hand-written one at a time.
Drift detection
A reconciler that continuously compares expected reality against observed reality, and raises a flag when they diverge.
See the world, not the code
Timeline and plan views built for a domain expert to read — not just an engineer.
Why customers care
What you get, in plain terms.
| What they get | What it means |
|---|---|
| Wrong output never becomes a wrong action | The blocking happens at the action boundary, not in a review meeting. |
| Every decision is auditable | A signed record of what was claimed, what evidence backed it, what was checked, who approved. |
| Evidence, not vibes | Every claim points at the exact page and line it came from. |
| It learns from real outcomes | Not from thumbs-up ratings — from the customs status, the payer response, the incident. |
| Rehearsal before commitment | See where a plan breaks before anyone acts on it. |
| Runs where the data lives | Deploys inside your own environment. Data residency is designed in, not bolted on. |
| No rewrite | Sits on top of your existing agent stack. |
See it in practice
A cross-border filing, start to finish.
Not hypothetical — this is the shape of the work, on our flagship workflow.
Filing prepared
An agent prepares a customs filing from an invoice, packing list, bill of lading, and certificate of origin.
Checks run — three fire
The duty math is short because the wrong-dated tariff was used. The invoice and bill of lading disagree on Incoterm. The goods description doesn't sit well with the declared HS code.
Blocked and routed
The filing is sent to the broker with the failing values highlighted — the exact numbers that don't add up, not a generic error.
A human resolves it
The broker fixes two issues and overrides the third with a written reason.
The outcome comes back
The customs portal returns "cleared" — and that result is joined back to the run that produced it.
The number moves
The query rate has gone from 12% to 4% — and you can name the exact check that did it.
Same pressure, a different shape: beating demurrage
A shipment has six days of free time left. The system runs thousands of simulated rollouts and finds a plan a human broker wouldn't: file early and fix the origin certificate in parallel rather than in sequence.
Two days in, port congestion hits. The divergence is caught automatically, and the system recommends paying for priority examination to bring the odds back up. The human decides.
Same engine, other industries
A decision goes out, and days later somebody says yes or no.
That gap is the product. The same approach applies wherever that pattern shows up:
Oil & gas
Permits to work and daily drilling reports checked against isolation certificates and gas-test windows. Simulate an intervention plan and see which sequence breaks a safety barrier before anyone goes to site.
Banking & finance
Covenant math, exposure limits, and sanctions screening checked as of the right date. Run an origination pipeline against hundreds of stress scenarios.
Healthcare
Prior authorizations and coding checked against what's actually charted and the payer policy on the date of service.
Ecommerce
Listing and pricing rules, substantiation of claims like "organic." Simulate a flash sale and see exactly which SKUs will oversell.
IT services
Change and access requests checked as policy-as-code. Blast radius simulated against the dependency graph before merge.
BPO
Disclosure and authority checks on agent responses. Live per-case SLA breach probability with a suggested reroute.
What's shipping first
The honest roadmap.
We're early, and we'd rather say so than oversell a demo. Here's what's actually in progress, in order:
-
Gate: verification & audit core
The deterministic checking layer plus the human escalation screen. In active development now.
-
Flagship workflow, live
The cross-border filing workflow above, running against real engagement volume rather than a demo dataset.
-
World Model: planner & simulator
The declarative world, planner, and simulator — early R&D, building on what the Gate work surfaces in production.
Want to see this against your own process?
Tell us what you're working on and we'll reach out as soon as there's something real to show — no mailing list, just a direct note.
Get in touch