What does forward-deployed mean?

Our engineers work inside your environment: your repositories, your cloud accounts, your team's rhythm. We build with your real data and systems instead of handing over a prototype from the outside.

Do we have to change our stack or our AI provider?

No. We integrate with what you already run, and we design so you can switch model providers later without a rebuild.

Can you work alongside our existing engineers?

Yes. That is the point of Pair Mode, and every other engagement ends with code, documentation and a handover your team owns.

How do you handle our data and security review?

Everything runs in your accounts with least-privilege access. Each build ships with a security review pack covering data flows, personal data handling and prompt-injection testing, so your reviewers have what they need.

What if AI isn't the right answer?

Then the Opportunity Map will say so. A clear 'not yet' is a better outcome than a pilot that never ships.

Pune, India. We work async-first with written updates, so time zones become an advantage rather than a delay.

Forward-deployed AI

We come in. AI goes live. Your team keeps it.

Whatever your stack, we embed with your team, integrate AI where it actually pays off, and stay until it works in production. Or we pair with your engineers and help you do it yourselves.

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The problem

Demos are easy. Production is the job.

Most AI pilots stall between the demo and real use. They break against legacy systems, get worse at real volume, run without monitoring, or wait for months in security review because nobody planned for it. We plan for all of it from day one.

Engagements

Start where you are.

Each engagement is fixed in scope and runs inside your accounts. Most teams start by seeing a Working Demo, then map the opportunities or go straight to a Production Slice.

Before anything

Working Demo

Tell us the tools you use and the question you wish they could answer. We build a working AI demo on realistic sample data shaped like yours, and you see it running.

A clickable demo on sample data shaped like your tools, built the way we would build it for production
A walkthrough call to show it working and answer your team's questions
A short written plan: what the production build involves, and what could stop it
No access to your systems or real data needed
A fixed price agreed before we start, credited in full against the build if you go ahead

See it working before you commit to the build.

Start here

AI Opportunity Map

A fixed-scope look at your stack, data and workflows to find the few places where AI actually pays off.

Each opportunity comes with expected running cost, risk and the criteria for stopping
A working spike on your real data, not a slide
Credited against a build if you go ahead

If AI isn't worth it for you, we'll say so.

We build it

Production Slice

One AI workflow, live in production inside your stack, with the guardrails that get it past review.

Success criteria agreed before we write code
Evals, monitoring, cost limits and audit logs built in
A security review pack as standard: data flows, personal data handling, prompt-injection tests

Built to ship, not to demo.

Your team builds it

Pair Mode

For teams that want to build it themselves. We work alongside your engineers until the patterns stick.

Pairing, code review and architecture sessions
An eval harness and guardrail patterns your team owns
We step back once your team can run without us

We make your team faster, not dependent.

Then, ongoing

Run & Improve

Ongoing care after launch, because models, prices and your data keep changing.

Monitoring and incident response
Evals re-run whenever a model updates
Cost tuning and model swaps when something better or cheaper arrives

The work doesn't stop at launch.

Capabilities

What AI can do in your stack.

Grouped by the job it does for your business, not by the model behind it.

Read
Turn documents, invoices, contracts, CVs and emails into clean, structured data.
Answer
Search and copilots over your own knowledge, for customers and for your team.
Act
Agents that call your APIs and carry back-office workflows through to the end.
Decide
Triage, routing, classification, lead scoring and anomaly flags.
Talk
Chat and voice agents on your website, WhatsApp and phone lines.
See
Vision for receipts, ID documents, product photos and quality checks.
Connect
MCP servers that let AI agents like ChatGPT and Claude use your product.
Guard
Evals, guardrails, red-teaming and runtime monitoring for everything above.

How we work

Commitments on every engagement.

Success is defined first
We agree what 'working' means, and what happens next, before any code is written.
Security review is part of the build
Data flows, personal data handling and prompt-injection testing ship with the work, not after it.
Your accounts, your keys, your repo
Everything runs in your cloud and your codebase. Nothing is locked to us.
No single-model bet
Model calls go through one layer we can point at another provider, so a price rise or an outage means a configuration change, not a rebuild.
Cost per task, in plain sight
You see what each AI task costs to run, so there are no surprise bills.
Written, async updates
Clear written progress you can read on your schedule, wherever your team is.

Proof

AI we have put to work.

A production LLM pipeline we rebuilt for a client, and the observability research that shapes how we build every AI system.

Mailboxes
LLM parsing
Dedupe
Search
Shortlist

From AI-built prototype to production platform

Forward-deployed AIPlatform refactorOngoing care

Synigence Global: From AI-built prototype to production platform

Live in production since September 2026, with ongoing care

A recruitment platform built fast with an AI app builder, rebuilt to run reliably on real mail, real candidates and real deadlines.

AI agent
Kernel events
Correlate
Flag the undeclared
Live dashboard

Seeing what AI agents actually do

AI observabilityAgent securityIn-house R&D

Argus: Seeing what AI agents actually do

Research prototype

In-house R&D: a runtime observability layer that shows what an AI agent really did on a machine, not just what it reported.

Questions teams ask us

What does forward-deployed mean?: Our engineers work inside your environment: your repositories, your cloud accounts, your team's rhythm. We build with your real data and systems instead of handing over a prototype from the outside.
Do we have to change our stack or our AI provider?: No. We integrate with what you already run, and we design so you can switch model providers later without a rebuild.
Can you work alongside our existing engineers?: Yes. That is the point of Pair Mode, and every other engagement ends with code, documentation and a handover your team owns.
How do you handle our data and security review?: Everything runs in your accounts with least-privilege access. Each build ships with a security review pack covering data flows, personal data handling and prompt-injection testing, so your reviewers have what they need.
What if AI isn't the right answer?: Then the Opportunity Map will say so. A clear 'not yet' is a better outcome than a pilot that never ships.
Where are you based?: Pune, India. We work async-first with written updates, so time zones become an advantage rather than a delay.

We come in. AI goes live. Your team keeps it.

Demos are easy. Production is the job.

Start where you are.

Working Demo

AI Opportunity Map

Production Slice

Pair Mode

Run & Improve

What AI can do in your stack.

Read

Answer

Act

Decide

Talk

See

Connect

Guard

Commitments on every engagement.

Success is defined first

Security review is part of the build

Your accounts, your keys, your repo

No single-model bet

Cost per task, in plain sight

Written, async updates

AI we have put to work.

Synigence Global: From AI-built prototype to production platform

Argus: Seeing what AI agents actually do

Questions teams ask us

Tell us what you're trying to ship.