For
Funded SaaS
Funded Bangalore SaaS & product companies
Funded Bangalore SaaS teams move fast and usually rolled out coding assistants without a baseline, so they cannot say what worked. Chokmah starts with a free Reality Check, then ships one workflow in a sprint with a baseline and an evaluation harness: turning an unmeasured rollout into a measured one.
- The main risk for funded SaaS is speed without measurement, not slow adoption.
- METR found developers 19% slower with AI while believing they were 20% faster: a baseline problem (with a 2026 caveat).
- First wins are usually in engineering and support: codebase onboarding, ticket handling, model-bump evaluation.
- An agentic feature without an evaluation harness is a candidate for Gartner's 40% cancellation.
- Chokmah is a fit for teams that want to measure, not for pre-product teams or large in-house engineering orgs.
Where it hurts
Coding assistants rolled out with no baseline
The team turned on Copilot or a similar assistant across engineering and assumes it helped, because it feels faster. Nobody measured before-and-after on real tasks. METR's randomised trial is the reason that assumption is dangerous, not the reason to abandon the tools.
Speed hides the cost of unowned agents
A funded team ships agentic features quickly and discovers the exception path is where cost and latency live. The demo is cheap; production traffic with retries, tool calls and fallbacks is not, and nobody priced it.
No evaluation harness, so every model bump is a gamble
Without a versioned test set, the team cannot tell a regression from variance when it upgrades a model or changes a prompt. Incidents become mysteries and confidence erodes quietly.
Investor pressure to show AI, not to measure it
The board wants an AI story for the next round. That pressure rewards visible launches over measured outcomes, which is exactly the pattern that produces impressive demos and no durable advantage.
The market, with sources
A METR randomised trial found experienced developers were 19% slower with AI while believing they were 20% faster.
METR has since stated the result is historical and may not reflect current tools or workflows.
Gartner expects over 40% of agentic AI projects to be cancelled by end-2027: escalating cost, unclear value, weak risk controls.
What a funded SaaS team is actually dealing with
A funded Bangalore SaaS company moves fast, decides fast, and refers fast, which is why we work with them even though they are a secondary audience. The risk is not that they are slow to adopt AI. It is the opposite: they have already rolled out coding assistants and shipped agentic features, usually without a baseline, and now cannot say what any of it actually did.
This audience feels the METR result hardest. Not because AI makes every developer slower (that is not what the study says), but because the team is most likely to have assumed a speed-up it never measured.
Developers took 19% longer with AI while estimating they were 20% faster: self-reported and measured productivity pointed in opposite directions.
METR said in February 2026 that the result is historical and may not reflect current tools. We cite it with that caveat. The point survives the caveat: a funded team that never baselined its assistant rollout is trusting a feeling, not a number.
The three workflows we see most often
For product teams, the first agentic wins are usually inside engineering and support, where the work is frequent and the data already exists.
- •Engineering
Legacy codebase onboarding
Getting a new engineer, or an agent, productive in a large unfamiliar codebase: retrieval over code and docs, not code generation. See the legacy codebase onboarding scenario.
- •Support
Support ticket deflection, measured honestly
Deflection rate is the wrong primary metric; resolution quality and escalation rate are the ones that matter. We design for those.
- •Product
Agent evaluation before every model bump
A versioned test set so a model upgrade is a measured decision, not a gamble against production traffic.
What does not work here
What does not work for a funded SaaS team is more velocity without measurement. Shipping faster into an unmeasured workflow just reaches the wrong place sooner. And an agentic feature with no evaluation harness is one of the projects in Gartner's 40%.
Where we would start
With a free AI Reality Check to pick one real workflow, then a Workflow Sprint that ships it with a baseline and an evaluation harness attached. The legacy codebase onboarding scenario shows the shape.
Who this is not for
We are a fit for funded teams that want to measure, not for a pre-product team with no workflow to baseline yet, and not for large IT services firms or Fortune 500 MNC GCCs that run their own engineering enablement in-house.
Put a number on your AI rollout
Book a free Reality Check, pick one engineering or support workflow, and ship it in a sprint with a baseline and an evaluation harness, so the next board update is a measurement, not a vibe.
Relevant scenarios
Frequently asked questions
You cannot know without a baseline, and most teams don't have one. METR's randomised trial found experienced developers were 19% slower with AI while believing they were 20% faster: self-report and measurement diverged. METR later said the result is historical and may not reflect current tools, but the lesson holds: measure real tasks before and after rather than trusting the feeling of speed.
A number. Chokmah would baseline one real engineering or support workflow, then ship an improvement in a sprint with an evaluation harness attached, so future model and prompt changes are measured decisions rather than gambles against production traffic. The value is not more tooling; it is knowing what the tooling did.
Because without a versioned test set you cannot tell a regression from normal variance when you upgrade a model or change a prompt. Gartner attributes much of the 40%+ of agentic projects it expects to be cancelled by 2027 to inadequate risk controls, and an evaluation harness is the cheapest such control a product team can put in place.
Only if there is a real, recurring workflow to baseline. A pre-product team with nothing to measure yet is better served by shipping product than by an adoption engagement. Chokmah works best with funded teams that already have workflows and traffic and want to know, honestly, whether their AI investment is doing anything.
See what a two-week diagnostic finds
We interview your people, shadow two workflows, and score which three to automate, and which to leave alone.