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Cerebral Hacks

AI engineering for systems that need to work.

We design, build, and deploy AI systems around real business workflows — from intelligent agents and enterprise knowledge systems to the infrastructure that runs them in production.

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A model isn't a product.

A successful AI product needs much more than model integration.

It needs:

  • A useful workflow.
  • A clear experience.
  • Reliable software.
  • Measurable AI performance.
  • Production infrastructure.

We build all of it together.

What We Build

From AI-powered SaaS platforms to intelligent search, decision systems, and copilots.

01 — AI SAAS

AI SaaS

Complete AI-powered software products with the workflows and infrastructure users need.

02 — LLM APPLICATIONS

LLM Applications

Assistants, copilots, content systems, and AI-native applications.

03 — AI PLATFORMS

AI Platforms

Reusable infrastructure and APIs for teams building multiple AI experiences.

04 — INTELLIGENT SEARCH

Intelligent Search

Search experiences that understand intent, context, and relevance.

05 — RECOMMENDATION SYSTEMS

Recommendation Systems

Personalized recommendations powered by behavioral and contextual signals.

06 — AI DECISION SYSTEMS

AI Decision Systems

Decision-support applications combining AI, data, rules, and human review.

Product Development Process

A disciplined engineering progression from problem discovery to production launch.

01
DISCOVER

Understand the core problem

Understand the users, workflow, problem, and business outcome.

02
DESIGN

Define experience & architecture

Define the product experience and technical architecture.

03
PROTOTYPE

Validate core assumption

Build the smallest useful system and test the core assumption.

04
ENGINEER

Build the full-stack system

Build the complete product across frontend, backend, AI, data, and infrastructure.

05
EVALUATE

Benchmark performance

Test AI quality, usability, latency, cost, and reliability.

06
LAUNCH

Deploy and iterate

Deploy, monitor, learn, and improve with real users.

💡 Engineering Note: This kind of prototype → real-user validation → engineering → testing → deployment progression is also reflected in current AI product-engineering practices.

Product Stack

Curated, production-tested technologies chosen for maintainability and speed.

Product

Next.js · React · TypeScript

Backend

Python · FastAPI · Node.js

Data

PostgreSQL · Redis · Qdrant

AI

OpenAI · Gemini · Claude

Infrastructure

Docker · AWS · Kubernetes

What You Receive

Complete end-to-end deliverables upon launch.

You don't just receive a prototype. You receive a working, production-grade system your team owns and can continue building on.

✓Product architecture
✓UX-aware AI workflows
✓Frontend application
✓Backend/API layer
✓AI integration
✓Data and retrieval systems
✓Authentication and access control
✓Evaluation
✓Monitoring
✓Deployment
✓Documentation
CerebralHacks Engineering Project · Proof

From concept to working product.

Challenge

What was the product problem? A team needed to turn a complex AI workflow into a scalable product users could interact with reliably without hallucination risks.

Product

What did you build? A full-stack AI-native platform with intuitive human workflows, deterministic API integration, and real-time streaming interfaces.

AI

Where did AI actually create value? Structured generation, multi-provider model routing, semantic caching, and dynamic context pruning.

Engineering

What made it production-ready? Sub-500ms response times, 100% schema validation, tenant isolation, and automated evaluation pipelines.

Outcome

What changed? Shipped from prototype to production with zero regressions, complete operational observability, and full codebase transfer.

Product Architecture · Full-Stack Web App · LLM Pipelines · Production EvaluationTalk to our engineers

Have an AI product worth building?

Bring us the idea, prototype, or workflow. We'll help turn it into a product people can actually use.

Talk to our engineers
No sales pitch·Technical conversation·Clear next steps