Binu Babu
AI Product Leader @Techjays · AI you can hand real work to
Everyone has the same models now. Almost no one has AI they'd trust with real operational work. Closing that gap is what I do. I design Autonomous Operational Systems (AOS): agents that run the work end to end, while people set the goals, judge the exceptions, and own the outcome. The moat was never the model. It's knowing what to hand over, matching autonomy to how reversible a decision is, and earning trust one piece of evidence at a time.
Let's connect
If you have a promising agent demo but can't yet hand it real work, that gap is exactly where I like to work.
A few opinions, strongly held
Stop asking where AI fits. Start redesigning the work around autonomy, and design it to earn trust.
Autonomy follows reversibility
Match how much an agent can do to how reversible the decision is. Behind two-way doors, agents move fast and report. At one-way doors, closing an account, filing with a network, a person stays in the loop. The delegation test is simple: hand over the work only where the expected loss is lower than the value released. The right autonomy level is a design decision per decision, not a model setting.
Deciding is the premium; building is the commodity
When every team can call the same models and ship in a weekend, building stops being the moat. What compounds is judgment built into data, workflows and trust: knowing which operation to hand over, where work actually waits, and what a customer would never forgive. Everyone gets the same models. Not everyone gets the same judgment.
Trust is earned with evidence, not configured
Evals, logs, visible reasoning and undo are product features, not compliance add-ons. Autonomy isn't a toggle you flip; it's earned stage by stage, shadow, assist, limited release, scaled, each gated by evidence. And it's demoted without drama the moment the counter-metrics slip. Stepping back a level is a designed state, not a failure.
Design for failure, or the agent designs it for you
Your agent will be wrong. Treat every one like a new employee with a badge, a role and a budget: its own identity, permissions scoped to the job, hard spend caps, and a logged, reversible trail. Then storyboard the failure paths, escalation, degraded mode, kill switch, recovery, before launch. Guardrails are structural, not instructions bolted on afterwards.
The whole playbook
I wrote down the full framework I use to take operational work from “where can we use AI?” to a system people hand work to: discovery, delegation, guardrails, agentic UX, and the rollout that earns autonomy. Long, opinionated, and free.
Read the playbookHow I got here
These days I lead AI product at Techjays, where I own two flagship platforms, an AI Operations Intelligence Platform and a Context Layer for Enterprise, and hold portfolio-level review authority over the firm's AI work: three projects across five delivery teams. I fund what maps to a quantified financial outcome and cut what does not, using Value Causation Trees that tie every proposed intervention to the P&L before it earns headcount. I grew the AI product function from four people to eighteen and was promoted to Lead within eleven months. The hard part is almost never the model. It is deciding what a business can safely hand to agents, and designing the system so it earns that trust.
Before that I built the AI capability from zero at Bridge Global, owning architecture, vendor selection, and production delivery across three client engagements. I ran discovery with the actual end users instead of stakeholder proxies, which cut six weeks of scope before anyone wrote code, and set the data-quality, evaluation, and prompt standards the team kept using. The reliability work underneath, cutting hallucinations and latency by roughly half, was always in service of one thing: systems a client would trust in production.
I come at product from the founder side too. I started Socife Technologies and ran it for six years, owning the strategy, the P&L, hiring, fundraising, and the investor relationships, not just the code. I took it from zero to ten thousand users, made the hard call to pivot when the first model stalled, and learned that a sustainable business is its own kind of engineering, with its own failure modes.
Before that I co-founded College-Shore, an education platform connecting students with academic resources. That is where I cut my teeth on the less glamorous parts of building a company: finding a business model that actually holds up, landing partnerships with institutions, and earning the first users one conversation at a time. Most of what I know about go to market and growth started there, in the gap between a good idea and a paying customer.
The thread through all of it is the same. Strategy only matters once it survives contact with real customers, and the part I like best is the messy middle, deciding which work a business can safely hand over, and designing the system so it earns that trust one piece of evidence at a time.
Roles
AI Product Lead
AI Product Manager
Techjays
Jul 2025 to Present
Technical Lead, AI Integration
Bridge Global
Sep 2024 to Jul 2025
Founder and CEO
Socife Technologies
2019 to 2024
Co-Founder
College-Shore
2016 to 2018
Systems people hand work to
Operational systems I have owned from strategy through production, where agents run the work, people manage the exceptions, and the outcome is a number someone signed up for.
AI Operations Intelligence Platform
One decision surface for leadership: meeting intelligence, performance, project run-rate, revenue-bleed detection, and CSAT, unified so leaders read, decide, and act in one place instead of across a dozen dashboards. Specialised sub-agents run under an orchestrator, each with scoped tools.
Gave back roughly six hours a week per leader and turned scattered status work into a single operational view. I own its vision, roadmap, and commercial outcomes.
Context Layer for Enterprise
An EBITDA-weighted model of the organisation, its people, relationships, and decisions, with Value Causation Trees tying every node to a financial driver, so agents prioritise by causal impact on the P&L rather than by what merely looks relevant.
It is the substrate the firm now funds AI work against: every proposed intervention has to carry a quantified financial claim before it earns headcount. I own the product.
Memory Systems for Agents
The memory layer that lets agents carry context instead of starting cold: structured short-term, episodic, and semantic memory, with persistent per-user and per-team history across calendar, transcripts, delivery, and finance.
Part of a shared platform layer that stopped every engagement rebuilding its own stack and cut time-to-ship for new AI capabilities by over 40 percent.
Customer Support Agents
Production support agents across four enterprise SaaS customers, owning the full path from retrieval strategy through generation guardrails, measured by an evaluation framework for correctness, groundedness, and latency.
The first AI beachhead that proved the model and set the reliability bar, regression checks that catch hallucination drift before it ships, that later work inherited.
Latest writing
Notes on AI product strategy, LLM engineering, and shipping agentic systems.
Why Every Product Person Needs a Personal Website in 2026
Your personal website is the one product you can own end to end: positioning, distribution, conversion, and measurement. In 2026 that matters twice over.
AI Token Budgets Are Here. Usage Was the Wrong Metric.
Apple, Goldman, Uber and Accenture are capping AI spend. The caps treat a measurement problem as a cost problem. Track cost per finished outcome instead.
SaaS Isn't Dying. It's Being Demoted.
AI won't kill your CRM. It will demote it into a back-end capability, and the real fight is over who owns the layer that runs the business.
Working on something?
If you are figuring out which operational work to hand to agents, and how to design it so the business can trust the result, I am around. The first call is free and usually pretty useful.