Devesh JoshiCo-founder, product
Nine years building AI platforms serving 12,000+ engineers. LLM platforms, agentic systems (MCP), and multi-model safety evaluation.
Why we do not quote a rebuild you do not need
Replacing a working system is the expensive answer, not the thorough one. How to tell integration from replacement before anyone writes a proposal.
A rebuild is the easiest thing to sell
It is bigger, it is cleaner to scope, and it removes every constraint the vendor would otherwise have to work inside. It is also, most of the time, the wrong answer.
The uncomfortable version: a rebuild quote is frequently the vendor solving their own problem — unfamiliar systems are hard to price — by charging you to make the problem go away.
Three questions that settle it
Is the system wrong, or is the data in it wrong? A CRM nobody trusts is usually a data and process problem wearing a software costume. Replacing the software migrates the mess into a more expensive container.
Does it have an API you can reach? If the answer is yes, integration is on the table and the burden of proof sits with whoever wants to replace it.
Is anyone still using it? A system in daily use encodes years of decisions nobody wrote down. That knowledge does not survive a rebuild, and rediscovering it is the part that overruns.
What integration actually looks like
The runtime already exists — agent orchestration, retrieval, voice, the integration layer. What gets built for you is the wiring onto your booking rules, your record structure, your escalation path.
That is why a single use case stays commissionable rather than getting priced out of reach, and why a multi-system rollout does not start from an empty repository.
When we do say replace it
When there is no API and no supported path to one. When the vendor has announced end of life. When the compliance requirement it was built for no longer exists and the constraints are now costing you more than the system returns.
Three conditions. If none of them holds, we will tell you the rebuild is not worth quoting.
Topic Focus & Target Concepts
This is the work behind our AI and automation practice — agents with real grounding, voice intake, and retrieval that answers from your records rather than the model's training data.
Agents with real tool accessRunning into this in your own stack? Twenty minutes, no deck.
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