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 systemsEmergency Services

Emergency Intake Triage

An all-AWS generative voice AI on an emergency intake line. It treats every call as an emergency by default, catches only the confident non-emergencies and warm-hands them to the right service, and gives the dispatcher read-only context. It never allocates a unit.

Amazon ConnectAmazon LexNova SonicBedrockLambda

Emergency-firstConservative by default

DeflectConfident non-emergencies

Read-onlyDispatcher unit + ETA

The problem

An emergency intake line cannot afford a wrong call. Sending a real emergency to a non-emergency queue is the failure that matters most.

At the same time, a share of calls are confident non-emergencies that do not need an emergency response at all.

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

Assume emergency

Every call stays in the emergency pipeline by default. Any doubt keeps it there — the bias is always conservative.

Deflect the clear cases

Only calls the system is confident are non-emergencies get warm-handed to the appropriate service instead of an emergency response.

Inform, never allocate

For retained emergencies the dispatcher sees read-only unit and ETA context. The AI surfaces information; a person makes every allocation decision.

What shipped

Confident non-emergencies are routed to the right service, freeing the emergency pipeline for real emergencies.

The AI never ranks or dispatches. It informs the dispatcher and leaves allocation to a human.

Book a call →