Our forward-deployed engineers work inside your environment, next to the people who run the workflow. They get AI into production, show business impact fast and leave your team able to run it.
You get a dedicated pod on site, never a remote vendor.
Deployment 001
From ringing phone to rider booked.
3
languages on one call: English, Hindi and Hinglish
Read the storyDeployment 002. Open for whoever is next.
Build
AI Engineers
Engineers who ship your AI roadmap: agents, knowledge systems, LLM workflows, voice and integrations.
See AI Engineers DeployFDEs
Forward-deployed engineers who take a working pilot into daily use on your real data and systems.
See FDEs ValidateEval Pods
A small team that tests every release against your real cases and gives a clear verdict before it ships.
See Eval PodsA deployment pod that sits with your team
How we deploy
Scope
We find the right use case with you: one workflow and the number that defines success.
Embed
The pod joins your team at your site and works inside your tools.
Ship
Production, never a prototype. Nothing goes live until it passes an eval set built from your real cases.
For enterprises and SMEs
Get in touch
Tell us what needs to move from pilot to production.
Services / Build
AI engineers who build inside your team.
When the roadmap has more AI work than your team has hands, we embed engineers who ship it in your repos, to your standards.
What they build
- Agents that complete real tasks end to end
- Knowledge systems that search and answer over your documents
- LLM workflows inside the products you already run
- Voice AI for inbound and outbound calls
- Integrations with ERP, CRM and core systems
- Internal tools your operations team uses daily
Who it's for
Founders and engineering leaders with a clear AI roadmap and not enough people to ship it.
- Your backlog of AI features keeps growing
- Hiring for the role is taking months
- Your core team keeps getting pulled off product work
How it works
Scope
One call to agree the first piece of work and the metric it should move.
Embed
Engineers join your standups, your repos and your review process.
Ship
Small, frequent releases, each one reviewed by your team before it merges.
Questions
Do your engineers work at our site?
Yes, on site by default. Remote days are agreed with you up front.
Who owns the code?
You do. It lives in your repositories from the first commit and the IP is assigned to you.
Which models and vendors do you use?
Whichever fits the job. We work across the major model providers and open models, so your architecture stays yours.
AI Engineers
Get in touch
Put more hands on your AI roadmap.
Services / Deploy
Forward-deployed engineers who take your pilot live.
The demo worked. Getting it into daily use at your site is a different job: real data, real systems, real users. That's the job our FDEs do.
What they do
- Scope the deployment with the people who do the work
- Map the workflow as it really runs, exceptions included
- Connect the system to your data and systems of record
- Deploy inside your security and access rules
- Train the team who will use it every day
- Hand over runbooks and ownership
Who it's for
Enterprises and SMEs whose pilot stalled between proof of concept and production.
- The pilot worked on sample data and breaks on real data
- Nobody owns the integration work
- Your developers are stuck doing deployment
How it works
Scope
One workflow, one metric, agreed with the team that runs it.
Embed
An FDE joins your team at your site, inside your tools and processes.
Ship
The pilot goes to production once it passes an eval set built from your real cases.
Hand over
Your team runs it. We move to the next workflow or step back.
Questions
How is an FDE different from a consultant?
A consultant recommends. An FDE writes the code, deploys it and stays until your team runs it.
Do you need access to our systems?
Yes, under your access rules. You grant it, you can see what was done with it, and you can remove it at any time.
When should we bring one in?
When a pilot has proven the idea and nobody has the time to make it production grade.
What happens after handover?
Your team runs the system. If you want ongoing checks, an eval pod keeps watch on every release.
FDEs
Get in touch
Bring us the pilot that stalled.
Services / Validate
Eval pods that prove your AI works before it ships.
A small team that tests every release of your AI system against real cases and tells you plainly whether it is safe to go live.
What every report includes
- Pass rate on an eval set built from your real cases
- Failure examples with the full transcript or output
- Regressions against the previous release
- Safety and policy checks
- A clear verdict: ship, fix or hold
Who it's for
Teams with AI already in front of customers, or about to be.
- You can't say how often the system gets it wrong
- Every prompt or model change feels like a gamble
- Customers find the failures before you do
How it works
Build the eval set
We collect real conversations, documents and tickets and agree what a good answer looks like.
Test every release
People and automated graders run the system against the set each time it changes.
Report
You get the failures, the evidence and a verdict before anything ships.
Questions
What is an eval set?
A fixed collection of real examples with known good answers. The system is scored against it every time it changes, so you can see if a release got better or worse.
Can a pod test a system you didn't build?
Yes. We need to be able to run it and see its outputs.
Eval Pods
Get in touch
Know it works before your customers find out.
Deployment 001
How Kinetic AI put the restaurant phone on autopilot.
A voice agent that answers the call, takes the order in English, Hindi or Hinglish, collects payment, pins the delivery location and books the rider. Nobody at the counter picks up.
3languages on one call: English, Hindi and Hinglish
4delivery partners for automatic rider assignment
5single-purpose agents working on every call
2gates before any order is sent: payment and location
The model was never the hard part
Voice models could already hold a conversation. Taking a real order is harder: the right item from a long menu, a payment that clears, an address a rider can find.
Kinetic AI needed all of that to work on a live phone line, with real customers, and no person stepping in.
Every call gets answered
One call runs the whole way through. No one at the counter has to stop serving to pick up the phone.
Call answered
The agent picks up the outlet's inbound line.
Order taken
Items, variants and add-ons from that outlet's own menu.
Payment collected
The order waits until payment is confirmed.
Location pinned
The caller drops a pin through a WhatsApp link.
Rider assigned
Booked automatically with a delivery partner.
It speaks the way callers do
Callers in Bengaluru move between English and Hindi inside a single sentence. The agent handles English, Hindi and Hinglish on the same call.
Speech recognition is routed by language, so each one is heard by the engine that handles it best.
No address read out over the phone
Spoken addresses are where phone orders go wrong. The agent checks the pincode first to see whether the outlet delivers there, then sends a WhatsApp link so the caller drops a pin.
Two gates hold every order until it is safe to send: payment confirmed and location confirmed.
From order to rider with nobody in between
Once both gates clear, the system assigns a rider automatically across four delivery partners: Shadowfax, Ola, Rapido and Blitz.
What runs underneath
- An orchestrator with five agents: customer background, menu retrieval, order accepting, order processing and delivery
- A structured menu for each outlet, 20 fields per item, from variants and add-ons to allergens and taxes
- One outlet's menu alone runs to 189 items
- Evals built from real call traces at three layers: did the order complete, did the conversation go right, was the speech heard and spoken correctly
What broke, and what we changed
Production found three problems the demo never showed.
One model doing everythingA single speech-to-speech model handling ordering, payment and delivery at once made things up.Split the work across an orchestrator and five single-purpose agents.
Mixed-language callsThe first speech stack could not follow callers switching between Hindi and English mid-sentence.Changed the speech stack and routed recognition by language.
Pitch-shifted voiceA sample rate mismatch, 16 kHz against 24 kHz, made the agent sound wrong on live calls.Fixed with resampling on the backend. Nothing changed for callers.
How we did it
Scope
One outlet, one job: take a phone order from hello to rider booked.
Embed
The first version went live at a dessert outlet in HSR, Bengaluru, and was tuned on its real calls.
Ship
Each failure on a live call became a test case. The speech stack and the architecture both changed before it held up.
Hand over
It now runs as Kinetic AI's product. Each new outlet gets its own menu data and goes live on the same rails.
Deployment 002
Get in touch
Open. Yours?
The same pattern fits any business that takes orders or bookings by phone.
Blog
Notes from the field on getting AI into production, and the stories behind our deployments.
-
Article
How enterprises get more from their AI investment
Most AI spend goes into pilots. The return only shows up in production. Where the value leaks, and what to change.
Read -
Deployment story
How Kinetic AI put the restaurant phone on autopilot
A voice agent that takes the order, the payment and the delivery location, then books the rider. What broke in production and how it was fixed.
Read
Blog / Article
How enterprises get more from their AI investment
Most of the money spent on AI goes into pilots. The return only shows up once a system is in daily use. Here is where the value gets lost, and what we do about it.
The spend is on pilots. The return is in production.
A pilot proves a model can do a task on sample data. It earns nothing until people use it in their daily work, on real data, inside the systems they already run.
Many projects stop between those two points. The budget is spent and there is no result to show for it.
Four places the value leaks
- The use case had no number attached. It was chosen because it was easy to demo.
- Nobody owns the last mile. The vendor sold a tool, the consultant wrote a plan, and the internal team is busy.
- No one can prove it works. Every release is a risk, so leadership stops trusting the system.
- The users were left out. The people meant to rely on it were never part of building it.
What Deploy Pager changes
- We pick the use case by the number it moves. One workflow and one metric are agreed before any code is written.
- We put engineers on site. The pod works next to the team that runs the workflow, so integration problems surface early.
- We gate every release with evals. An eval set built from your real cases decides what ships.
- We measure against your own baseline. The result is reported in a number your business already tracks.
- We build your team's capability as we go. The people who will run the system work alongside the pod from the first week.
What it looks like in practice
Our first deployment, with Kinetic AI, is a voice agent that takes restaurant phone orders from the first hello to the rider being booked. It only held up after the speech stack and the architecture were changed in response to live calls. That is the work a pilot never gets to.
Where to start
Pick one workflow where AI should already be working, and decide the number you want it to move. That is enough for a first conversation.
For enterprises and SMEs
Get in touch
Tell us what needs to move from pilot to production.
For enterprises and SMEs
Tell us what needs to move from pilot to production.
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Now hiring
Apply
Join our founding team.
We're hiring engineers who want to work on site with customers and see their work go live.
What we look for
- You have shipped AI to production, beyond demos
- You are comfortable working on site with a customer's team
- You write things down: evals, runbooks, handovers
Apply
Tell us what you've shipped.
Say which of our three teams you'd join: AI Engineers, FDEs or Eval Pods.