Capabilities
Every AI Capability, Production-Ready
We build AI systems for every layer of the stack — from model selection and fine-tuning to deployment, monitoring, and cost optimization.
LLM Application Development
Production chatbots, AI copilots, document Q&A, code assistants, and multi-turn conversation systems with memory, guardrails, and cost controls.
Autonomous AI Agents
Multi-step task execution agents with tool use, web browsing, code execution, API integration, and human-in-the-loop oversight for enterprise safety.
RAG & Knowledge Systems
Retrieval-Augmented Generation pipelines connecting LLMs to your enterprise data — PDFs, databases, wikis, and APIs with semantic search and citation.
Computer Vision Systems
Object detection, image classification, OCR, video analysis, defect detection for manufacturing, and real-time surveillance intelligence pipelines.
Voice AI & Speech Systems
Real-time speech-to-text, AI voice assistants, call analytics, voice cloning, IVR intelligence, and multi-language transcription with sub-500ms latency.
Predictive ML & Analytics
Demand forecasting, churn prediction, recommendation engines, anomaly detection, and time-series forecasting models deployed as production APIs.
Our Process
How We Build AI for Production
Every AI system we ship goes through a rigorous engineering process — from model selection and data architecture to observability, cost controls, and production hardening.
Not a Proof of Concept
We don't build demos you can't scale. Every engagement targets production — with latency budgets, fallback strategies, and monitoring from day one.
01
Discovery & Use Case Scoping
Define the AI problem, data sources, success metrics, latency requirements, and compliance constraints before writing a single line of code.
02
Model Selection & Architecture Design
Choose the right foundation model, embedding strategy, retrieval architecture, and AI framework based on your accuracy, cost, and latency requirements.
03
Rapid Prototype → Evaluation
Build a functional prototype, run evals against your benchmarks, and iterate on prompts, retrieval quality, and model choice before full build.
04
Production Engineering & Integration
Build the full system with API layers, caching, rate limiting, fallback logic, streaming, and secure integration with your existing stack.
05
Deploy, Monitor & Optimize
Deploy with LLM observability (LangSmith, Helicone, Weights & Biases), cost dashboards, drift detection, and an optimization roadmap for post-launch.
Technology Stack
AI Technologies We Work With
Foundation Models
GPT-4oClaude 3.5Gemini 1.5Llama 3MistralPhi-3
AI Frameworks
LangChainLlamaIndexLangGraphCrewAIAutoGenDSPy
Vector Databases
PineconeWeaviateQdrantpgvectorChromaMilvus
MLOps & Observability
MLflowLangSmithWeights&BiasesHeliconeArize
Real-World Use Cases
What AI Systems We Build for Businesses
Internal Knowledge Chatbot
Chat with company docs, policies, and procedures using RAG + LLM
AI Customer Support Agent
Autonomous support with ticket triage, resolution, and escalation
AI Code Review & Generation
PR review agents, docstring generators, and test writers
Document Intelligence Platform
Extract, classify, and query unstructured data across thousands of docs
Manufacturing Defect Detection
Real-time vision AI on production lines with <100ms latency
AI-Powered Sales Assistant
Lead scoring, proposal generation, and CRM data enrichment
Voice-Enabled AI Interface
Real-time voice AI with <500ms latency for web and mobile apps
Predictive Analytics Engine
Demand forecasting, churn prediction, and anomaly detection APIs
Fine-Tuned Domain Models
Custom LLMs trained on your data for medical, legal, and finance
Start Your AI Project
Book a Free AI Architecture Audit
Tell us what you need to build. A senior AI engineer will review your use case, recommend the right stack, and give you a realistic delivery estimate — free, no obligation.
45-Minute Technical Call
With a senior AI engineer, not a sales rep
Architecture Recommendation
Model selection, stack advice, and risk flags
Realistic Delivery Estimate
Timeline, team size, and cost ballpark before you commit
What Happens Next
01
Discovery Call — 45-min session with a senior AI engineer to map your requirements and data sources
02
Architecture Plan — Recommended model stack, integration approach, risk assessment, and cost estimate delivered
03
Development Starts in 24h — Sprint zero kicks off within 24 hours of sign-off, first working module delivered within the week
Our Guarantee
Every AI engagement ships with a 90-day warranty. If anything we built breaks due to our code, we fix it at no cost — no questions asked.
Chat with our engineers nowTalk to an AI Engineer
// free 45-min call · no commitment
FAQ
Common Questions About AI Development
Everything you need to know. Can't find what you're looking for? Talk to us
We build LLM-powered applications, autonomous AI agents, RAG systems, computer vision pipelines, voice AI interfaces, and predictive analytics platforms — all production-ready with monitoring and observability from day one.
We work with OpenAI GPT-4o, Claude 3.5, Gemini 1.5, Llama 3, Mistral, and Phi-3. Frameworks include LangChain, LlamaIndex, CrewAI, AutoGen, and LangGraph for agentic workflows. We select the best fit for your latency, cost, and accuracy requirements.
A single-purpose AI agent typically takes 3–6 weeks from requirements to production. Multi-agent orchestration systems with tool use, memory, and complex decision trees require 8–14 weeks depending on scope.
Yes. We build on-premise or private cloud AI systems using open-source models (Llama 3, Mistral, Phi-3) via Ollama or vLLM, ensuring data never leaves your environment. Ideal for healthcare, finance, and legal applications with strict data compliance requirements.
Yes. We handle the full fine-tuning pipeline: data preparation and cleaning, fine-tuning (LoRA, QLoRA, RLHF), model evaluation against your benchmarks, and deployment on your preferred cloud or on-premise infrastructure.
Ready to Ship Your AI System?
Book a free architecture audit with a senior AI engineer. We'll scope your project, recommend the right stack, and give you a delivery timeline — no sales pitch.