Product Growth
Decide what to build, who it's for and why it wins, with named owners and a board-ready plan.
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For leadership teams at growth-stage and PE-backed companies
Better decisions. Built into your business.
We refine strategy, build AI capabilities, and deliver real outcomes.
No deck, no sales pitch. A working conversation about how to achieve results.
Latest case study · Professional Services
Improving Decision Quality in AI-Enabled Product Teams
A leading global strategy consulting firm's teams had AI access to a year of project history — but not a reliable way to know which decisions still governed the work. Reusable AI workflows made decisions more consistent and reduced repeated review.
Read the case study, opens in a new tab Latest thinking · October 2026AI Can Read Your Files. Who Decides Which Ones Are Right?
Access permissions determine what AI may read — not which business decisions are current, why they changed, or who owns them. Why decision ownership, supported by AI, is the operating fix, drawn from a recent engagement with a global strategy consulting firm.
Read the essay, opens in a new tab How we workBuild. Operate. Transfer.
Start with Design & Prove on one consequential workflow. Finish with a capability your team owns and runs without us.
See how we work, opens in a new tab
The problem
You don't need another AI strategy, proof of concept, or agent demo.
If any of these sound familiar, you're in the right place.
01
Your team uses AI. Decisions still don't stick.
Drafts come back polished, but they revive options you already rejected and ignore constraints you set last week.
02
Your pilots never become operations.
Proofs of concept impress, then stall without an owner, a baseline, or a business case.
03
AI is adding work, not removing it.
People spend their time reconciling sources, rechecking outputs and rebuilding context at every handoff.
04
The board wants results, not another roadmap.
You have the tools and the budget. What's missing is a workflow that performs, with numbers to show it.
The solution
We redesign the workflow, not just the tools.
You need a better way to run the work that delivers outcomes, with a team that can manage it. That’s where we start.
One shared decision record, with an owner.
We make the current decision, its owner and the reason it changed explicit, and route open questions to whoever is accountable. Your people and their AI tools work from the same approved context.
Our point of view (opens in a new tab)Pilots built to become operations.
Design & Prove sets the baseline, owner and business case before anything is built. Then we run the workflow on real work until the results hold, and hand it to your team.
Case study: 12 months of planning, down to weeks (opens in a new tab)AI does the legwork. People make the calls.
We redesign who does what: AI researches, analyzes and coordinates; people decide and approve. Reconciliation and rework get designed out, not added on.
Case study: improving decision quality (opens in a new tab)Results measured from day one.
Every engagement starts with a baseline and is judged on business outcomes: decision quality, cycle time, cost and reliability. Not on how many agents we deploy.
See client results
How we work
Build → Operate & Optimize → Train & Transfer
Most firms stop at the handoff. We run the capability on real work until it performs, then make sure your team can run it without us.
Start here: Design & Prove
We pick one consequential workflow, measure how it runs today, and prove the approach on real work before you commit to the build.
1
Build
We redesign how your people and AI agents share the work: who decides, what AI researches, analyzes and coordinates, and where exceptions go. Then we implement it in your systems, with your data and constraints.
In practice: Improving decision quality in AI-enabled product teams
You getA working AI-enabled business process. Not a deck. Not a demo.
2
Operate & Optimize
We run it on real work alongside your team. We measure outcomes, find where it breaks, and tune responsibilities, approvals, quality, cost and controls until the results hold.
You getA proven capability with defined performance and economics.
3
Train & Transfer
We train the people accountable for it and hand over the workflows, agents, decision history, integrations, evaluation methods, SOPs and metrics, against handoff criteria agreed up front. The decisions and context behind the workflow live in teamctx, our open-source context layer, in your own repository.
You getA capability your team owns and improves without us.
The scaffolding comes down. Ongoing optimization is available if you want it. It's never required to keep the capability running.
What we hold ourselves to
People keep the consequential decisions.
AI does the research, analysis and coordination, and flags uncertainty instead of guessing.
Ownership is explicit.
A named owner is accountable for each decision and outcome. Agents are never the authority.
Business results are the scorecard.
Decision quality, cycle time, cost and reliability. Not how many agents we deploy.
You end up not needing us.
Support is optional, never a condition for the capability to keep working.
Where we apply it
GTM System
A working go-to-market engine, deployed in your business and handed to your team.
See GTM System →AI Optimization
AI that pays back: readiness, data foundations, AI cost, and function-by-function deployment.
See AI Optimization →
Client impact
Outcomes, not output.
Our most recent client work, newest first.
12 months of innovation planning, down to weeks.
A Fortune 500 apparel and lifestyle company benchmarked agentic AI against its own 18-month innovation process. A 40+ agent system, grounded in proprietary data, compressed roughly 12 months of planning and cross-functional coordination toward weeks — and leadership operationalized it.
40+Orchestrated agents
Months → weeksPlanning compressed
AdoptedInto the workflow
Pilots don't pay rent. Five expansions did.
A healthcare platform turned stuck pilots into five enterprise expansions, lifting pilot → Phase 1 conversion by 80%.
5Enterprise expansions
80%Pilot → deploy lift
StrongerExecutive buy-in
18-month deals, closed in 6.
A healthcare platform cut enterprise cycles from 18 to 6 months and killed 90% of stalled deals with a three-role qualification framework.
18 → 6Months, sales cycle
90%Fewer stalled deals
StrongerStakeholder alignment
Growth made the portfolio unshippable. One model fixed it.
A healthcare company consolidated independent modules into a Digitize → Analyze → Optimize platform, launched in 6 months with 80% lift in market alignment.
6 MonthsPlatform launch
80%Market alignment lift
ClearMulti-product vision
The product was fundable. The story wasn't.
A hospital systems platform rebuilt its narrative into a Land → Expand → Deepen growth story and closed investor ambiguity across product, GTM, and financials.
1 MonthInvestor-ready
StrongerBusiness model
ImprovedInvestor engagement
The product was fragmented. The pipeline needed one story.
A healthcare platform unified fragmented products into a single multi-stakeholder enterprise narrative and opened $12M+ in qualified pipeline.
$12M+Pipeline accelerated
UnifiedPlatform narrative
FasterDeal cycles
Industries we know
- Healthcare technology
- Hospital systems
- Fintech
- Ad tech
- Travel technology
- Professional services
- Real estate technology
- Apparel & consumer goods
How we engage
Clear scope. Your ownership. No lock-in.
Commercial terms
- Fixed scope, milestone-billed. No hourly billing, no open-ended retainers.
- Start small. Design & Prove on one workflow before a larger commitment.
- You own the result: workflows, agents, context, SOPs and metrics.
- Ongoing support is optional.
Trust and governance
- People keep consequential decisions, with a named owner for each one.
- Built in your systems, with your data and operating constraints.
- Decision history in your own Git repository.
- No lock-in. The context layer is open source.
Working across jurisdictions? Read: The sovereignty tax
Open source
teamctx
AI-native version control for team context. The decisions, rules and assumptions behind the work, approved by the people accountable for them and available to every AI tool your team uses. It's how a handoff keeps its reasoning.
- MIT licence
- Git-backed
- Manager-approved
- CLI + MCP
PE operating partners: prove a capability in one portfolio company, then adapt it across the rest.
Latest thinking
What we're seeing in AI, operating models and growth.
Can Claude or Copilot Bring Your Agency In-House?
Only 12% of brands have AI fully integrated into marketing workflows. Why the real question isn't what to cut from agency scope, but which capabilities should become core — and the barbell reshaping agency fees.
AI Made Building Easy. It Didn't Make Ownership Cheap.
AI made building cheaper. It did not make ownership cheap. Why the real cost of enterprise AI sits across human oversight, orchestration, infrastructure and operations — and a framework for deciding what to build, buy, partner for or rent.
The Sovereignty Tax: Why AI Is Rewriting the Economics of Global SaaS
SaaS was built on pooling everything across one global deployment. AI and jurisdictional regulation just broke that premise — six sovereignty design levers, four hyperscaler bets, and the tiered architecture that turns the tax into a moat.
The Missing Layer in Enterprise AI: Why Every Company Needs an AI Operating Model
AI capability is no longer scarce. Organizational legitimacy is. Why the bottleneck in enterprise AI has shifted from model access to the operating model that decides who answers for what an agent does.
Our team
Senior operators, not generalist consultants.
Shikhin Agarwal
Founder & CEO
25 years in tech and AI platforms.
Former P&L Executive at VC-funded social media, ecommerce, fintech, digital healthcare, and consumer SaaS companies.
Rajeev Singh
Founding CTO, Advisor
25+ years in tech and AI
CTO at modern platforms, startups, e-commerce, fintech, AI, and blockchain.
Balaji Gopalan
CPO, Advisor
30+ years in tech, software and AI.
B2C and B2B across security, social, dev tools, edtech, martech, media, healthcare.
FAQ
Questions buyers ask us
What does StatsLateral do?
We build AI-native operating capabilities inside your business. We redesign a critical workflow so your people and AI agents make better decisions and execute faster, run it on real work until it performs, then train your team and transfer ownership.
How does an engagement start?
With Design & Prove. We select one consequential workflow, establish a baseline, and validate the approach on real work before you commit to a larger build.
What do we own at the end?
Everything needed to run and improve the capability: workflows, agents, project context and decision history, integrations, evaluation methods, SOPs and operating metrics. Ongoing support from us is optional.
How are engagements priced?
Fixed scope and milestone-billed against named deliverables. No hourly billing and no open-ended retainers. We scope on the first call; a statement of work typically follows within 5 business days.
Do we have to buy teamctx?
No. teamctx is our open-source (MIT) context layer for decisions, assumptions and ownership. Where it fits the workflow, we use it so that context transfers with the capability and stays in your repository after we leave. There's no licence to buy.
Who do you work with?
Leadership teams at Series B–D and PE-backed companies, typically $20M–$200M in revenue and 100–1,000 people, in the US and EU.
How is StatsLateral different from McKinsey, Bain, or BCG?
Senior operators only. Fixed scope with no hourly billing. We build and run a working capability rather than hand over recommendations, and we design every engagement so you stop needing us.
Pick one decision that matters.
How long would it take your team, and its AI tools, to find the current version, who owns it and what it affects? If the answer isn't obvious, that's where we'd start.
Book a 30-minute conversation