Custom Software &
Production AI for
Complex Operations

RAG (Retrieval-Augmented Generation) connects a large language model like GPT or Claude to a vector database containing your own documents. When a user asks a question, the system first searches your documents for the most relevant content, then generates an accurate, cited answer from that content, not from the AI's general training data. This eliminates hallucinations because the AI only answers from your verified information. We build RAG systems using LangChain, Qdrant, and LangGraph.

A chatbot answers questions in a conversation. An AI agent is autonomous, it plans, uses tools, calls APIs, makes decisions, and completes multi-step tasks without human intervention. A chatbot answers 'What is your return policy?' An AI agent can process the return, update your inventory system, issue a refund, and send a confirmation email, all triggered by a single message. We build AI agents using LangGraph for stateful, auditable execution.

We implement multiple layers: strict retrieval grounding (the LLM can only use retrieved content, not its training data), citation enforcement (every answer must cite its source), confidence scoring (low-confidence answers trigger fallback responses), and production monitoring with LongTracer, our open-source hallucination detection tool with 49k+ downloads. We also run regression testing with LongProbe before every deployment.

Most engagements follow our 4-step process: AI discovery and architecture in 1–2 weeks, a validated prototype in 2–4 weeks, then build, test, and deploy over 4–12 weeks depending on scope. A focused RAG system (document ingestion, vector database, retrieval API, and chat interface) typically lands in production within that overall window. Timeline depends on document volume, required accuracy, integration complexity (CRM, SSO, existing tools), and whether you need a custom UI or an embeddable widget. We deliver working builds every 2 weeks via agile sprints.

Yes, this is one of our most common engagements. We add AI-powered search, document assistants, recommendation engines, and workflow automation to existing SaaS platforms without a full rebuild. We work within your existing stack and deployment pipeline. Most AI feature additions take 4–8 weeks. We also ensure the features are monitored and tested in production, not just demoed.

A free 30-minute review of your existing RAG system or AI agent. We check: retrieval accuracy (is it finding the right content?), hallucination rate (is it making things up?), latency (is it fast enough for real users?), cost per query (is it economically viable at scale?), guardrails and access control (is sensitive data protected?), and integration reliability (does it break on real documents?). You get a written summary of what's working, what's broken, and what to fix first. No sales pitch.

For AI/RAG: LangChain, LangGraph, Qdrant, OpenAI, Claude, Gemini, Hugging Face. For backend: FastAPI (Python). For frontend: Next.js, React, Tailwind CSS. For database: Supabase/PostgreSQL. For deployment: AWS, Vercel, Docker, GCP. For mobile: Flutter. We choose tools based on your requirements, not a fixed template.

Yes, over 90% of our clients are in the US, UK, UAE, Australia, Singapore, and Germany. Our team operates with timezone overlap across US, UK, and Middle East business hours. We use agile sprints with bi-weekly video demos, shared project dashboards, and async communication on Slack or Teams. Location is never a barrier to quality delivery.

Cost depends on scope: document volume, integrations, accuracy targets, and whether you need a custom UI or an embeddable feature inside an existing product. Focused RAG or AI-feature engagements commonly start in a defined MVP range and scale with complexity; multi-agent workflow systems and deep ERP/CRM integrations cost more. We scope after a discovery call so you get a clear build plan and timeline before committing, not a generic package price.

Yes. We can embed as a dedicated squad for ongoing AI and software delivery, or augment your existing team with senior engineers for a defined workstream. Engagements typically include architecture ownership, sprint delivery, and production support, not just ticket-level staffing. We work inside your tools (Slack/Teams, your repos, your CI) with timezone overlap for US and UK teams.

We define evaluation criteria before build: retrieval recall on your documents, grounded answer rate, citation coverage, latency, and cost per query. Prototypes are stress-tested early; production systems get regression suites (including LongProbe) and monitoring (including LongTracer) so accuracy is measured continuously, not only in a demo. Low-confidence answers can be blocked or routed to human review. We also offer a free AI reliability check to baseline an existing system before you invest in fixes.