# Kafa Software: Production AI, engineered end to end. Kafa Software is an AI engineering and consulting company. We design, build and run production AI end to end: RAG and knowledge systems, LLM applications and agents, evaluation, LLMOps and the data pipelines underneath, so you get working AI without first hiring an AI team. We are also the team behind Frame, the open-source IDE for AI coding agents. Website: https://kafasoftware.com Contact: https://kafasoftware.com#contact ## The problem we solve Every company wants AI in production. Very few can staff it. Forward-deployed and AI engineering roles are among the hardest to fill in tech. The work spans data engineering, ML, backend and product, and the people who can do all of it are rare. So most AI initiatives stall somewhere between the demo and the deployment. - The hiring gap: Open AI engineer roles stay open for months. Meanwhile the roadmap waits, and competitors ship. - Prototype purgatory: A demo works on ten documents. Without evals, data plumbing and monitoring, it never survives real users. - Nobody owns the whole thing: The model vendor, the data team and the app team each own a slice. The pipeline between them is where projects fail. Kafa owns the whole pipeline. We build in your stack, run it in production, and hand over a system your team can operate, with the knowledge to grow it. ## Services ### AI strategy & consulting Find the use cases worth building, and the ones that aren't, before the budget is spent. - Use-case discovery & ROI - Feasibility & risk review - Build vs. buy - Architecture & roadmap ### AI in your product We add AI features to your existing products and internal tools, built to your codebase, conventions and security rules. - Copilots & smart features - Built in your stack - Code review & documentation - Knowledge transfer ### RAG & knowledge systems Answers grounded in your own data, with citations, permissions and freshness built in. - Ingestion & chunking - Hybrid search & reranking - Source citations - Access control ### LLM applications & agents Assistants, copilots and agents that call your tools and finish real workflows. - Tool use & MCP - Multi-step agents - Structured outputs - Human-in-the-loop ### Evaluation & LLMOps Know whether it works, and keep it working, with evals, tracing and cost control. - Eval suites & regression tests - Tracing & observability - Latency & cost budgets - Guardrails ### Data & integration pipelines The plumbing that makes AI useful: connectors, ETL and vector stores wired to your systems. - Connectors to CRM, ERP & docs - Batch & streaming ETL - Vector databases - APIs & webhooks ### Model selection & fine-tuning The right model for the job: frontier APIs, open-weight models, or a fine-tune of your own. - Model benchmarking - Fine-tuning & distillation - Self-hosted inference - Routing & fallbacks ### Security, privacy & governance AI your security team signs off on: private deployments, audit trails and injection defences. - VPC & on-prem deployment - PII handling - Prompt-injection defence - Audit & compliance readiness ## Approach 01. Discover: We map your workflows, data and constraints, then rank use cases by value and feasibility. You get a clear plan, not a slide deck. 02. Data foundations: Sources are audited, connected and cleaned. We define what “correct” means and build the first evaluation set from real examples. 03. Build: Retrieval, prompts, tools and agents are built in your stack, in short iterations, against the eval set from day one. 04. Evaluate: Accuracy, groundedness, latency and cost are measured on every change. Nothing ships on vibes. 05. Deploy: Into your cloud or on-prem, with authentication, rate limits, guardrails, tracing and dashboards in place. 06. Operate & hand over: We monitor, tune and document. Your team owns the system, and knows how to extend it, when we step back. ## Principles - Model-agnostic: We pick models on evidence, not partnerships, and design so you can switch. - Your data stays yours: Private deployments by default when the data demands it. No training on your data. - Measured, not guessed: Every system ships with an evaluation suite your team can run. - Built to hand over: Clean code, docs and training. You are never locked in to us. ## Frame, built by Kafa Software Spec-driven development for AI coding agents. Frame is our free, open-source desktop IDE for AI coding agents. A spec becomes the plan an agent follows, a unit of work that can run in parallel, and durable project context that survives every session. We built it for our own engineering, and we use it on every engagement. - Durable project context that every session loads - Specs → plans → tasks, executed by agents - Parallel agents in isolated git worktrees - Works with Claude Code, Codex CLI and Gemini CLI Website: https://frame.cool Source: https://github.com/kaanozhan/Frame (Apache-2.0) ## Technologies we work with - Models: Claude, GPT, Gemini, Llama, Mistral, Qwen - Orchestration & agents: LangGraph, LlamaIndex, MCP, Pydantic AI, Vercel AI SDK - Retrieval & data: pgvector, Qdrant, Elasticsearch, PostgreSQL, Airflow, dbt - Evaluation & observability: Langfuse, Ragas, OpenTelemetry, Promptfoo - Serving & infrastructure: vLLM, Ollama, Kubernetes, AWS, Google Cloud, Azure - Languages & frameworks: Python, TypeScript, FastAPI, Next.js ## FAQ ### What does Kafa Software do? Kafa Software is an AI engineering and consulting company. We design, build and operate production AI systems end to end: retrieval-augmented generation (RAG), LLM applications, AI agents, evaluation and LLMOps, and the data pipelines behind them. We act as one partner from the first use case to the running system. ### Do we need to hire forward-deployed AI engineers to get AI into production? Not necessarily. Forward-deployed AI engineers, who take AI from prototype to production inside a company's own environment, are among the hardest roles to hire. Kafa delivers that outcome as a partner: we design, build and deploy the system end to end in your infrastructure, then hand it over with documentation and training. ### Can you build a RAG system on our private data without it leaving our infrastructure? Yes. We regularly deploy retrieval, embedding and inference inside a customer's own cloud account (VPC) or on-premises, including with self-hosted open-weight models, so sensitive data never leaves your environment. ### Which LLMs and AI vendors do you work with? We are model-agnostic. We work with frontier APIs such as Claude, GPT and Gemini, and with open-weight models such as Llama and Mistral served through vLLM or Ollama. We choose models by benchmarking them on your own evaluation set and design systems so the model can be swapped later. ### How long does it take to get an AI use case into production? It depends on data readiness and integration depth. With AI-native tooling, a working prototype on your own data comes quickly, often within days; the production system then follows in short, measured releases. We scope each engagement before committing to a timeline. ### How do you measure whether an AI system actually works? Every system we build ships with an evaluation suite built from real examples. We measure accuracy, groundedness, latency and cost on every change, trace production traffic, and alert on regressions, so quality is measured rather than assumed. ### We already have engineers. Do you work alongside an existing team? Yes. We work in your repositories and infrastructure, review code with your engineers and transfer knowledge throughout, so your team can own and extend the system after handover. ### Who builds Frame? Frame, the free and open-source desktop IDE for AI coding agents at frame.cool, is built by Kafa Software. It brings spec-driven development and durable project context to Claude Code, Codex CLI and Gemini CLI, and it is the same tooling we use on client engagements.