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 systemsContent Integrity

AI File Detection

An ensemble classifier running on serverless infrastructure that flags AI-generated submissions, surfaced inside the case page so reviewers see the signal where they already work.

BinocularsONNXAWS LambdaLWC

AutomaticFlagging

In-pageWhere reviewers work

FallbackAs methods evolve

The problem

More submissions were arriving with AI-generated content. Reviewers needed a way to know which ones to scrutinize.

They needed it without leaving the page where they already work, and without trusting a single brittle detector.

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

Ensemble detection

Multiple detection methods are combined, so no single brittle signal decides the call.

Serverless compute

It runs on serverless infrastructure, scaling with submission volume.

In-page signal

The flag shows up inside the case page reviewers already use, with a fallback path as detection methods change.

What shipped

AI-generated submissions get flagged automatically, where reviewers already work.

The ensemble plus a fallback path keeps it useful as detection methods evolve.

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