Forward-deployed AI engineers embed with your team and turn one real workflow into AI agents running in production.
Every name here is an engagement we delivered — the same engineers who did that work are the ones who show up for your build.
BCGConsulting01
HPEEnterprise IT02
Schneider ElectricEnergy management03
Wispr FlowVoice AI04
FreshworksSaaS05BCGConsulting01HPEEnterprise IT02Schneider ElectricEnergy management03Wispr FlowVoice AI04FreshworksSaaS05
AccelVenture capital06
ShellEnergy07
mem0AI memory08
FloCareerHiring09
CodeYoungEducation10AccelVenture capital06ShellEnergy07mem0AI memory08FloCareerHiring09CodeYoungEducation10Our engineers work in the tools you already run — your cloud, your repos, your data, your ticketing. Nothing gets rebuilt somewhere else, and your team does not have to adopt anything new to keep it running.
The usual way
How we work
Six real projects, running inside client companies today. Here is what each one does and how it is built.
The demo is the easy part. What kills projects is everything after it — integrations, permissions, edge cases, evaluations, and nobody accountable for the thing running on a Monday morning.
of enterprise GenAI pilots deliver no measurable P&L impact
MIT, State of AI in Business, 2025
fewer than one in ten GenAI POCs in India ever reach production
EY–CII, AIdea of India, 2025
of companies abandoned most AI initiatives in 2025, up from 17%
S&P Global, 2025
No parallel workstreams and no discovery phase. We pick one workflow, then take it all the way.
We sit with your team for a few days and choose one workflow worth automating.
We build against your actual data and the cases that break things. You see it running at the end of every day.
It goes live next to your current process, so you can compare the two before anything depends on it.
We document it, train whoever will run it, and step back. The code is yours.
Not advisors reviewing your work from the outside. The people who know the workflow, build the system, and make it dependable sit inside the work with your team.
Knows the workflow, the edge cases, and the decision that matters. Turns a vague request into an outcome the team can measure.
Builds the agents, retrieval, model calls, and system connections directly in your repo and against real data.
Turns a promising demo into a dependable system with evaluations, permissions, logs, and the failure paths production exposes.
Thirty minutes. Bring one workflow that costs your team real hours. We'll tell you on the call whether it's worth building — and we say no more often than we say yes.