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 systemsChild Welfare

Service Matching Engine

A matching engine that scores child-welfare cases against a structured catalog of evidence-based therapeutic models, recommends the best fit, and shows the reasoning behind it.

GPT-4oCustom MetadataLWCSalesforce

Best fitRecommended per case

ShownReasoning exposed

Human-ledFinal decision

The problem

Matching a family to the right evidence-based therapeutic model was manual and judgment-heavy, and the decisions carry real consequences.

Consistency across cases was hard to hold when every match was made from scratch.

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

Structured catalog

Evidence-based therapeutic models are held as structured, queryable data rather than buried in documents.

Score the case

Each case is scored against the catalog to recommend the best-fit model.

Show the reasoning

The recommendation comes with its reasoning, so it supports the caseworker instead of hiding the logic. The decision stays human-led.

What shipped

Consistent, explainable recommendations that support the decision instead of replacing the human making it.

The same catalog and logic are applied across cases, with the reasoning visible every time.

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