Production-Grade Reliability
Error handling, fallback chains, cost monitoring, and observability built in. AI systems that run 24/7 without human babysitting.
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We build production-grade AI systems — LLM integrations, autonomous agents, RAG pipelines, n8n automation, and data engineering infrastructure. From prototype to enterprise scale, without the hype.
Most AI projects fail at deployment. We engineer reliable, observable, cost-efficient AI systems that run in production, not just demos.
Error handling, fallback chains, cost monitoring, and observability built in. AI systems that run 24/7 without human babysitting.
We build around abstractions — swap GPT-4 for Claude or Llama without rewriting your app. Future-proof from day one.
ETL pipelines, vector stores, data lakes, and feature engineering. AI is only as good as the data it runs on — we build both.
We define success metrics before writing a line of code. Every AI system ships with dashboards proving it delivers business value.
From LLM wrappers to autonomous agents and enterprise data platforms — we build it all.
GPT-4, Claude, Gemini, or open-source LLMs (Llama, Mistral) integrated into your product. Fine-tuning and prompt engineering included.
Multi-step AI agents that browse the web, execute code, call APIs, and complete complex tasks with minimal human oversight.
Retrieval-Augmented Generation over your documents, PDFs, databases, or internal wikis. Accurate answers grounded in your data.
AI-powered workflow automation with n8n, Make, or custom orchestration. Connect 400+ services with intelligent decision logic.
Reliable data pipelines with Airflow, dbt, or custom Python. Real-time streaming (Kafka) or batch processing — built for scale.
Churn prediction, demand forecasting, fraud detection, and recommendation engines trained on your data and deployed to production.
Pinecone, Weaviate, Qdrant, or pgvector for semantic search and RAG. We design the embedding pipeline and retrieval strategy.
Context-aware chatbots and product copilots that understand your domain, integrate with your CRM, and hand off to humans seamlessly.
We audit your data quality, volume, and structure. We define what AI can and cannot do for your use case — no overselling.
We design the AI architecture and build a proof-of-concept in 2–3 weeks. You see results before committing to full development.
Iterative development with rigorous evaluation. Accuracy benchmarks, cost profiling, latency testing, and safety guardrails.
Production deployment with full observability — token costs, latency, accuracy drift, and automated alerts when quality drops.
We work with OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3.5), Google (Gemini), and open-source models (Llama 3, Mistral, Mixtral). We design model-agnostic architectures so you can switch providers without rebuilding your application.
RAG (Retrieval-Augmented Generation) lets an LLM answer questions based on your own documents, databases, or knowledge base — rather than relying on training data alone. You need it if you want AI that knows about your products, policies, or internal data.
We implement evaluation pipelines that measure accuracy, hallucination rate, and latency on every release. We use techniques like chain-of-thought prompting, output validation, confidence scoring, and human-in-the-loop fallbacks.
Yes. We build intelligent automation using n8n, custom Python orchestration, or LangChain agents. Common use cases include automated document processing, email triage, lead scoring, report generation, and customer support automation.
A simple LLM integration or chatbot starts from $8,000. A full RAG system with custom data pipeline runs $20,000–$50,000. Complex AI agents or ML model development start from $40,000. We always start with a scoped PoC to reduce risk.
We architect for data privacy from the start. This includes using on-premise or private cloud LLM deployments, configuring OpenAI/Anthropic data retention policies, anonymising PII before it reaches the model, and implementing access controls.
Yes. We build integrations with your CRM (Salesforce, HubSpot), ERP (SAP, Odoo), databases, internal APIs, and third-party services. AI becomes a layer on top of your existing stack — not a replacement.
It depends on the use case. For RAG and chatbots, structured documents, PDFs, or databases are enough. For predictive models, we typically need 12+ months of historical transaction or event data. We run a free data audit to assess readiness.
AI & Data Engineering
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Read case study ->Tell us your use case, your data, and your goal. We'll respond within 24 hours with a PoC proposal and a realistic assessment of what AI can deliver.
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