What to build vs how to build

Shipping ≠ Coding

Shipping has two parts:

1. Deciding what to build
2. Writing code for it

Deciding what to build is collaborative and cross-functional and includes non-technical team members who never open an IDE but are key to decision making. For example -

• Customer-facing teams contributing what users need: features, products, feedback
• SMEs who govern the business logic and rules to implement
• Product managers who decide what ships and own the roadmap

Access to a coding agent does not make the above simpler or faster. It includes taking 100s of decisions, weighing the pros and cons, getting everyone on the same page. The token maxing approach of just asking an agent to generate 100s of prototypes does not work. Nobody builds a building by creating 100 buildings and keeping the one they like. You plan it, then build. Code at scale is no different, the rework is the expensive part both in terms of team's time and token cost.

Deciding what to build comes first and shapes everything downstream. We own that. Coding agents and developers build it.

Deciding what to build - a successful project requires the following key pieces:

• Understand the vague requirements and capture intent
• Be able to collaborate with various personas (CS, UIUX, PM, Engineering and SMEs)
• Foresee and account for dependencies, blockers, edge cases and risks
• Provide context to varied parties - UI/UX for design, to developers, to coding agents
• Be able to change direction mid way, update requirements
• Pull in context from multiple sources, (Customer requirements, sales calls etc)

Agents write code, not standardize process

Coding agents are really good at understanding and writing code. But they do not collaborate.

• For example a team of 100 will generate 10 .md files on a daily basis, collectively creating 1000 .md files with no way to sync across the organization, keep them updated and hence no use beyond just one developer
• Not easy to track dependencies across multiple disjoint services. Coding agents only know what they can see from code they have access to
• Continuous 24x7 monitoring across every source, not just code but sales calls, tickets and decisions, flagging impact proactively before anyone asks. A coding agent only answers when prompted, about the code it can see
• Coding agents are not targeted towards optimizing or enforcing a process across organizations, that is a different product in itself, we are building that
• Even where agents could be configured for this, almost no one does. Setup is heavy, and 80% of users across our customer base - especially the non-technical stakeholders who drive these decisions - never get past basic prompting.

A better model won't fix this, it's a process and collaboration gap.

Market validation

Enterprises are already realizing the importance of the above process and are trying to solve for this, including some of our customers as well. They are already using coding agents for development, but are still trying to solve this. This proves a clear need for ProdE - an intelligent and collaborative planning brain that sits before coding agents.

Frontier labs run on free tokens, so they build 100 prototypes and keep one - and sell that as the standard. Your team pays list price for it. The planning layer can only come from a vendor paid for efficiency, not consumption.

Uber burned its entire AI budget in four months. Their answer was a standardized process, an internal AI reviewer that checks a PRD for completeness and downstream impact before any code starts: uber.com/blog/first-pass-prd. The big orgs are solving this first because they have the resources, most teams don't.

Why It Can't Come From the Labs

We don't compete with Claude or Cursor on code quality. We operate in a layer they won't build, and wouldn't be trusted to own if they did. You can't ask the people selling you tokens to help you reduce token consumption, the same way the company selling you gas won't develop a car which uses less gas. A layer that cuts token use hits their revenue directly, so it's the last thing they will build. Our incentive is the opposite: we only stay worth paying for by making you more efficient.

• They sell tokens. Their research is geared towards making the models better. A planning layer that makes every downstream token burn more effective will never be their flagship
• Better models do not mean cost optimization - increasing intelligence is also linked to increasing costs, organizations are becoming more and more cost conscious, we have seen this across every customer conversation we have had
• Enterprises want control and neutrality + on prem deployments etc. Which includes hosting their own models in many cases. No one lets the vendor selling tokens govern how tokens get spent

Open-weight models are catching the frontier at a fraction of the cost. The token-maxing bet is wearing thin, and teams want neutrality more than before. That shift is only accelerating, and it's exactly what we help teams do: optimize tokens, optimize process, deterministic results.

Meta and Amazon both killed their internal AI-usage leaderboards within days and told staff to stop using AI just to use it. Spend does not equal output, and the biggest teams are now saying so.

The plan is the asset. It owns what to build, in a deterministic, process-driven way. Who builds it will keep changing, some teams on Claude, some on Codex, some on whatever comes next, and that's exactly where the industry is going.