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-5.5Claude Opus 4.7Gemini 3.1 ProLlama 4MistralGemma 4
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
Related Services
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-5.5, Claude Opus 4.7, Gemini 3.1 Pro, Llama 4, Mistral, and Gemma 4. 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 to 6 weeks from requirements to production. Multi-agent orchestration systems with tool use, memory, and complex decision trees require 8 to 14 weeks depending on scope.
Yes. We build on-premise or private cloud AI systems using open-source models (Llama 4, Mistral, Gemma 4) 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.
AI development costs $10,000 to $150,000 depending on scope. A simple AI chatbot or automation runs $5,000 to $20,000, a custom LLM or RAG application $20,000 to $80,000, and an enterprise AI platform $150,000 and up. See our full AI development cost breakdown.
Yes. We provide AI consulting to scope the right approach, then build custom AI solutions tailored to your data and workflows, from LLM apps and RAG systems to AI agents and computer vision, not one size fits all software.
Yes. We deliver AI development services for US and UK companies as a senior offshore team, with timezone overlap, NDA and IP assignment, and transparent pricing at a lower cost than a US in house build.
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.