Move from scattered experiments to AI that runs inside daily work. We train your people on the tools that matter, audit how your teams actually work, pick the highest-value targets, and direct the rollout until it sticks.
01 · TRAINING
On-site and hands-on. Your team applies AI to its own workflows and ships working improvements in the room.
02 · CONSULTING
We audit how work actually happens, pick the highest-value targets, set the architecture and guardrails, and review what ships. You end up with AI your organisation owns and can extend.
THE GAP
Licences and isolated pilots are not adoption. Adoption means teams know what is worth doing, can build it into real workflows, can judge the output, and can keep improving without an outside agency.
59%
report an AI skills gap
Leaders, 2026 — even after two years of AI tools in the building.
// Tredence, 2026
76%
adopt AI when employers train them
Versus 25% when they're left to figure it out alone.
// Bright Horizons, 2026
53%
AI talent shortfall in India
Projected by end of 2026 — Hyderabad's GCC hiring market is among the tightest in the country.
// NASSCOM / Mercer-Mettl
Figures above are as reported by the cited sources.
THE EXPERIMENTATION TAX
Teams lose months testing tools without a shared standard for value, safety, or production readiness. We bring the patterns we learned shipping AI products in production, then help your team apply them to the work that matters.
// WITHOUT DIRECTION
// WITH AGENTOPS TECH
Automation agencies build a black box you rent. We build your team's ability to run AI themselves, and that compounds long after the engagement ends.
WHO IT'S FOR
Leaders need a sequence, engineers need sound technical direction, and every function needs the judgement to use AI well. We align all three so adoption survives beyond a pilot.
Your engineers learn the patterns behind reliable agents, eval harnesses, RAG, and production deployment — then apply them to your stack.
Your team turns briefs, research, landing pages, and variants into repeatable workflows without giving up judgement or brand voice.
Your operators learn to identify safe automation, design human review, and reclaim time from triage, routing, reporting, and SOP upkeep.
Your people team learns where AI can draft, structure, and summarise — and where human judgement must remain in control.
Your reps build better research, account briefs, follow-up, and call-summary workflows around the CRM they already own.
Your teams apply AI to analysis, commentary, ticket review, and risk signals with explicit checks for accuracy and accountability.
WHAT WE TEACH
We start with your workflows and tailor every session to them. No off-the-shelf course and no outsourced build. These four layers give your team a repeatable way to adopt AI on its own.
// LAYER 01
Find the workflows worth changing first. Estimate value, risk, readiness, and ownership before anyone reaches for a model.
// LAYER 02
Know when to use AI, which model fits the job, how to challenge an output, and when a human must decide.
// LAYER 03
Turn repeat work into reusable tools and multi-step agents. Your team builds on its systems, data, and constraints while we teach and review.
// LAYER 04
Define what good looks like, what to log, what to review, and what never goes to a model before your engineers deploy.
FORMAT
All on-site at your office, or hybrid if you're distributed. Pricing on request — depends on team size and scope.
HALF-DAY
Leadership · 5–20
Align leadership on what is real, what is noise, where value sits, and which decisions must come first. Ends with a shared direction.
2 DAYS
One function · 15–40
Hands-on training around one function's real work. The team learns the method and applies it to workflows it owns in the room.
1 WEEK
Multiple teams · 40–150
Training plus advisory across several teams. We set direction and review; your engineers build and deploy on your systems.
ONGOING
Fractional CAIO · rolling
Office hours, architecture and workflow reviews, adoption roadmap, and a champions programme. Senior AI judgement on call as the rollout scales.
WHAT YOU TAKE HOME
Your people own the method, the decisions, and the work. We leave behind capability, not a dependency on us.
⚙
Training uses real workflows, tools, and constraints. Your people do the building; your engineers decide what reaches production.
★
A shared way to find opportunities, prototype, evaluate, govern, and improve — usable long after the session ends.
▲
A prioritised sequence with owners, risks, and next decisions so the organisation knows where to focus.
✦
People inside each team who can coach others, review new use cases, and keep the programme moving without us.
THE TEAM
FOUNDER · AI ENGINEER & RESEARCHER
Builds and ships AI end to end. Haku, a native macOS agent that operates real apps, and Haku Terminal, a multi-agent workbench. A real-time voice engine in Rust serving production speaking practice. Language models trained from scratch and deployed on-device, with the research published on arXiv. Speaks at MLX India meetups on designing models for Apple silicon.
LINKEDIN →ADVISOR · MICROSOFT RESEARCH
Principal PM at Microsoft Research. GenAI, multimodal, conversational systems. Helped build a medical AI model trained on millions of images. ICML Outstanding Paper. Multiple US patents.
LINKEDIN →WHAT WE'VE SHIPPED
Everything we train on and advise about, we have built and run in production ourselves: agents, real-time voice, evaluation pipelines, on-device models. The playbook comes from shipping, not from slides.
Haku: a native macOS agent that operates real apps through their own interfaces. An on-device model reads hundreds of screen elements and hands the agent the seven that matter. Shipped, v1.2.
// Agents that act reliably inside real software, not demos.
Haku Terminal: a workbench where Claude Code, Codex and other agents converse, delegate, share memory and search past work over one protocol. 30 tools, v1.0, in daily use.
// The operating model for teams of agents. The Ops in AgentOps.
A real-time voice engine in Rust: WebRTC, interruption handling, a from-scratch end-of-turn model that answers in 2 to 5 ms. Serves live speaking practice today. A bridge that lets an AI agent answer WhatsApp Business calls.
// Voice for support and sales, with the failure modes already found.
The Smart Language and Supertutora: a learning platform in nine languages. LLM content generation with automatic quality checks, planner and evaluator agents, and simulated users that run whole journeys against the API before real ones do.
// Evals and quality control for AI output at scale.
A 203M-parameter mixture-of-experts pretrained natively in ternary. Runs on the Neural Engine of an Apple Watch. The measurement study behind it is on arXiv (2608.22110, Aug 2026).
// When data cannot leave the building, we know what runs on-device.
Ten write-ups at lab.haku.sh on small models and speech on Apple silicon, each stating its method and its limits. Talks at MLX India meetups in Hyderabad and Bengaluru.
// We measure before we recommend.
PROCESS
// 01
CONSULT
We learn the goal, the teams, the constraints, and where adoption is stuck.
// 02
AUDIT
We observe how work happens and find the opportunities worth pursuing first.
// 03
PRIORITISE
Together we set the roadmap, owners, success measures, and guardrails.
// 04
TRAIN + DIRECT
Your team builds. We teach, guide technical choices, and review the work.
// 05
HAND OVER
Your champions, roadmap, and playbook keep the work moving after we leave.
BOOK A CONSULT
Tell us what your teams have tried and what is getting in the way. We'll recommend training, consulting, both, or tell you plainly if neither is the right move yet.
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