What does Buildify do?

Buildify is a founder-led applied AI studio. We design, build, and ship production AI systems for organizations with complex or regulated workflows, end to end, from architecture to the running system.

What kind of work does Buildify take on?

Document and extraction pipelines, validation and compliance review, decision support and matching engines, and AI integrated inside platforms like Salesforce. The common thread is work where wrong answers have real consequences.

How is a production AI system different from a demo?

A demo calls a model and shows a happy path. Production is everything around it: the pipeline, the guardrails, the human-in-the-loop handoff, and the system it lives inside. The model is about 10 percent of the work; Buildify builds the other 90 percent.

How does Buildify handle the risk of wrong AI answers?

Systems are designed to flag what they are unsure about and route those cases to a human, rather than hand over a confident wrong answer. Recommendations show their reasoning so a person can verify them.

How many systems has Buildify shipped?

Six production AI systems are live in regulated environments, spanning government procurement, compliance review, case management, and child-welfare decision support.

Who builds the work at Buildify?

Buildify is founder-led. Kalpesh Mahida builds the systems end to end: architecture, prompts, infrastructure, interface, and deploy, solo or embedded with a small team.

← All systemsCase Management

Speech & Document to Case

Speech or document input is transcribed, structured, and turned into a case record automatically, inside the platform the team already works in.

SalesforceGeminiApexLWC

AutomaticCase records built

In-platformNo new tool

ZeroManual re-typing

The problem

During intake, case workers listened to calls and read documents, then re-typed the information into the case system by hand.

It was slow and error-prone, and it pulled attention away from the person they were actually helping.

How it works

Extract, verify, then route the unsure to a human.

route_to_human.pipeline Confident · auto Unsure · to a human

Incomingcases · docs

Modelconfidence

Human review

Flagged cases

Auto-resolved

Confident, cited

Capture

Calls and documents are transcribed and parsed into structured fields automatically.

Build the record

The structured output becomes a case record directly, with no manual data-entry step.

Stay in the platform

It all happens inside the tool the team already uses, so there is nothing new to learn.

What shipped

Real-time intake without the manual data entry step, in a tool the team already knew.

The case record forms as the call or document is processed, not after.

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