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 systemsGovernment Procurement

RFP Analyzer

A two-model pipeline that reads government RFP PDFs, extracts the requirements, deadlines, and rubrics, then has a second model review the extraction and flag anything it is unsure about.

GeminiClaudeCloud RunFirestoreReact

< 15 minFrom a multi-day manual review

Every lineCited back to its source

Two modelsOne extracts, one verifies

The problem

Government RFPs run long and dense. A single solicitation can carry hundreds of requirements, scattered deadlines, and a scoring rubric buried in an appendix.

Reviewing one by hand took a team multiple days. A missed requirement or a misread deadline is not a small mistake in procurement. It can disqualify a bid.

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

Read & extract

The first model reads the full RFP PDF and pulls out every requirement, deadline, and rubric line into a structured record, each one cited to the page and passage it came from.

Review & flag

A second model reviews the extraction against the source. Anything it is unsure about, an ambiguous clause, a conflicting date, gets flagged rather than guessed.

Route to a human

Flagged lines surface to a reviewer with the citation alongside. The confident extractions pass through. The human spends their time only where judgment is actually needed.

What shipped

A review that took days now takes under fifteen minutes, and every extracted line points back to where it came from in the document.

The system does not replace the reviewer. It does the reading, shows its work, and hands back the few decisions that still need a person.

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