AI Operations for Growth-Stage Teams
Helm your business.
Don't leave AI to hope.
You've adopted AI faster than you've instrumented it. Every team uses a different tool, nobody can prove what's actually working, and the outputs drift. I install the measurement layer underneath.
Where teams get stuck
Four drift patterns.
Where to start
You've kicked the tires. Nothing's stuck.
Every vendor pitches the AI angle. Your team half-uses ChatGPT. Someone piloted Copilot. You keep drifting between options without committing to one. You need a framework for deciding what to pilot first — and how to know it worked before you scale.
AI-tool sprawl
Your BDRs use three different AI tools.
Everyone drafts differently. No shared templates. No way to know which messages are actually winning. The output looks fine — nobody can prove it's working.
No measurement layer
You adopted AI faster than measurement.
Great outputs, zero attribution. When someone asks "did the v2 prompt actually help?" nobody has an answer. The dashboard tells you activity is up. It doesn't tell you whether the AI is getting better.
Volume without lift
AI is doing more work but pipeline isn't moving.
Your team writes faster, replies at scale, generates more sequences than ever. The top-line hasn't budged. The output drifted before it compounded.
Fit
Who this is for.
You'll get the most from this if several of these describe you today.
Real AI usage across your team
GTM, RevOps, or Ops functions are already using Claude, ChatGPT, Gemini, or similar tools — daily, not experimentally.
Fragmented tools, no shared standard
Each writer picks their own tool. No shared templates. No agreement on what "good" output looks like.
You track activity, but can't attribute changes
You know your reply rate, meeting-booked rate, or forecast accuracy. You can't tell which AI change moved the needle.
You want ownership, not dependency
You'd rather build the muscle in-house than lease the fix from another vendor. Your team should own the playbook.
You're growth-stage
The motion is working, but drift is starting to show. Seed-stage is too early; enterprise is a different problem.
How we work
Four ways in.
Every engagement is me and your team. No delivery pool, no rotating juniors, no handoffs. That's why I take on two or three at a time.
Where to start
Readiness
2–3 weeks · fixed scope
You know you need to move on AI but the surface is too big. I map your workflows by AI leverage, recommend a first pilot with success metrics defined, and set the guardrails. Natural on-ramp to Embedded or Scoped once the pilot lands.
Big goals, big systems
Embedded
Ongoing · 1–2 days a week
You've got real drift across multiple functions. I work alongside your team, build the measurement layer inside your existing tools, and transfer the playbook. Your team owns it by day 30.
One clear problem
Scoped diagnostic + build
6–10 weeks · fixed scope
One workflow — BDR enablement, content library, forecast layer, or another discrete surface. Diagnose the drift, build the fix, hand off the operating manual.
You're building — I sanity-check
Thinking partner
Monthly retainer · async + one call/week
Framework and pattern-recognition support for teams doing the build in-house. Best when you have the operator; you want a second brain on the architecture.
The Helmur thesis
Helm, don't hope.
"But lately, I'm beginning to find that I should be the one behind the wheel."
— Incubus, Drive
I named this company Helmur years ago because I believed operators need to helm their businesses — not leave outcomes to hope.
Two years into working with AI-augmented teams, that thesis is truer than ever. AI is powerful. It's also non-deterministic. If you're not measuring what it does, you're not helming — you're hoping.
The work is closing that gap: the templates, the correction data, the attribution. Deterministic where consequences matter. AI where it's genuinely better than a rule. Every human edit captured as data the next iteration learns from.
About
Prashant Kaw
20 years in GTM operations and demand generation. Most notably ex-HubSpot. Built and shipped systems at every growth stage from seed to scale.
I've spent the last two years going deep on AI-augmented operations — the shared templates, the correction data, the attribution loops. I work directly with growth-stage companies on the operating system underneath their AI tools.
Deep operator experience at fractional cost. Framework support without the enterprise consulting overhead. Your team owns everything by the end.
Diagnostic
Ready to helm?
A 30-minute call where I do the work of thinking, not the work of pitching.
The 30 minutes covers:
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Audit your AI-tool sprawl
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Identify where measurement is missing
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Map the highest-leverage first fix
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Recommend engagement shape if we're a good fit