AI-Powered Engineering
Custom AI & Intelligence.
Engineered for Production.
We help startups and enterprises move beyond simple API wrappers. From private RAG document search and custom LLM workflows to predictive ML and MLOps — built with privacy, speed, and accuracy.
100% Code & IP Ownership
You own all repository access, intellectual property, and infrastructure from Day 1.
Senior Lead Engineers Only
Direct pair programming with senior engineers. Zero handoffs to junior offshore pools.
Weekly Shipped Sprints
Live staging builds delivered every Friday. You test real progress, not slide decks.
Guaranteed 24h Response SLA
Direct Slack / WhatsApp channel with fast, transparent communication on business days.
Six AI Practices
From data audit to production MLOps.
Six specialized disciplines to help you build, deploy, and govern intelligent software safely.
RAG & Enterprise Knowledge Retrieval
Turn your company's scattered docs into instant, citation-backed answers.
We design high-accuracy Retrieval-Augmented Generation (RAG) pipelines. By indexing your internal PDFs, databases, Notion workspaces, and support tickets into vector stores, your team gets instant, hallucination-free answers with direct source citations.
Key Deliverables
- Vector store setup (Qdrant, Pinecone, or Supabase Vector)
- Hybrid search (Dense vector + Sparse keyword matching)
- Automated document chunking & re-ranking pipelines
- Source citation & attribution tracking
- Role-based access control (RBAC) data filtering
RAG & Enterprise Knowledge Retrieval
Production-grade architecture with zero data leakage guarantees and full code ownership.
Conversational AI & Autonomous Agents
Context-aware AI assistants that solve customer & internal workflows.
We engineer conversational AI agents that understand multi-turn context, run actions via tool-calling, and integrate directly with your CRM, Slack, or customer support desks.
Key Deliverables
- Multi-turn conversational memory engines
- Tool-calling agents for CRM & API automation
- Streaming chat UI for Web & React Native
- Human-in-the-loop fallback mechanisms
- Multi-modal image & voice query handling
Conversational AI & Autonomous Agents
Production-grade architecture with zero data leakage guarantees and full code ownership.
AI Copilots & Workflow Automation
Embed AI directly into your operational tools to eliminate manual work.
Stop copying and pasting into ChatGPT. We build embedded AI copilots inside your internal dashboards and mobile apps that extract invoice data, summarize medical records, or audit legal contracts automatically.
Key Deliverables
- Embedded dashboard & mobile AI sidebars
- Automated PDF, image, & spreadsheet parsing
- Structured JSON data extraction pipelines
- Contract, invoice, & document triage
- Automated summary & report generators
AI Copilots & Workflow Automation
Production-grade architecture with zero data leakage guarantees and full code ownership.
AI Strategy & Technical Audits
Senior guidance to choose the right models and avoid overspending.
Not every feature needs an expensive LLM. We audit your product roadmap, evaluate model tradeoffs (OpenAI vs Anthropic vs self-hosted Ollama/vLLM), and design cost-effective architectures.
Key Deliverables
- Model selection & latency/cost trade-off matrix
- Open-source vs API LLM evaluation
- Token budget & prompt optimization audit
- Data privacy & regulatory compliance assessment
- AI architecture blueprint & POC scope
AI Strategy & Technical Audits
Production-grade architecture with zero data leakage guarantees and full code ownership.
Predictive Machine Learning & Custom Models
Transform historical data into accurate forecast and decision models.
When generative AI isn't the tool, custom predictive ML is. We train, tune, and deploy classic ML models for time-series forecasting, customer churn prediction, and automated recommendation engines.
Key Deliverables
- Custom regression, classification, & forecasting models
- Feature engineering & data cleaning pipelines
- Real-time prediction API endpoints
- Model retraining & drift detection triggers
- Dashboard visualizations for predictive outputs
Predictive Machine Learning & Custom Models
Production-grade architecture with zero data leakage guarantees and full code ownership.
MLOps, Evaluation & Governance
Production monitoring to ensure long-term accuracy and security.
AI applications degrade without monitoring. We build continuous evaluation pipelines (Ragas, LangSmith) that test prompt updates, monitor hallucination rates, and protect against prompt injection attacks.
Key Deliverables
- Automated prompt evaluation & benchmark suites
- Hallucination & toxicity monitoring dashboards
- Prompt injection & security guardrails
- CI/CD regression testing for AI outputs
- Token usage & API cost alert systems
MLOps, Evaluation & Governance
Production-grade architecture with zero data leakage guarantees and full code ownership.
A clear path from data audit to live AI
Four phased stages. We benchmark accuracy and cost before you write a single line of production code.
We audit your data sources (PDFs, Postgres, Notion, Slack), evaluate security requirements, and select the optimal model & vector strategy.
We index your documents, build the retrieval pipeline, and benchmark answer accuracy, latency, and token costs against real queries.
We bind the AI backend to your custom Web, Mobile, or Dashboard surface with streaming responses, error fallbacks, and RBAC security.
We set up automated prompt testing, hallucination monitoring, and evaluation pipelines to keep answer quality high as your data grows.
AI Engineering FAQ
Common questions about data privacy, RAG vs fine-tuning, costs, and IP ownership.
How do you ensure our corporate data remains private when using AI?
We strictly implement zero-data-retention API contracts, private vector store indexing (Qdrant/Pinecone), and optional self-hosted open-source models (Llama 3, DeepSeek via Ollama/vLLM) running on your isolated cloud infrastructure. Your data is never used to train public models.
What is the difference between RAG and fine-tuning, and which do we need?
RAG (Retrieval-Augmented Generation) connects an LLM to your real-time company knowledge base for accurate, citation-backed answers without retraining. Fine-tuning teaches a model a specific style, tone, or specialized syntax. 90% of enterprise use-cases start and succeed with RAG.
How long does it take to ship a working AI Proof of Concept (PoC)?
A fully functional, context-grounded RAG or LLM prototype typically takes 2 to 4 weeks. By Week 2, you are testing a live staging interface with your own documents and real data.
Who owns the IP for custom AI pipelines and prompts?
You own 100% of all code, custom prompts, vector embeddings, fine-tuned weights, and pipeline architectures from day one. There are no proprietary lock-in fees.
Can custom AI tools be integrated into our existing mobile or web app?
Yes. We build typed API layers (tRPC, REST, WebSockets) that connect custom AI backends seamlessly to React Native mobile apps, Astro/Next.js web applications, or operational dashboards.
Ready to build AI software that holds up?
A 30-minute technical call is usually enough to assess feasibility, choose the right model architecture, and outline a 4-week prototype.