Five services. Production-grade AI, engineered for your environment.
Kalman AI is a specialist AI engineering firm. Every engagement is led by engineers who have built, deployed, and operated AI systems in production. We work alongside your team, transfer knowledge, and leave you with capabilities you own — not a dependency you rent.
What we build for you.
LLM Integration & API Setup
Production-ready LLM integrations with cost controls, fallbacks, and observability built in.
Read more →№ 02AI Agent Development
Single-task agents to coordinated multi-agent networks — with evals, audits, and human-in-the-loop controls.
Read more →№ 03RAG & Knowledge Systems
End-to-end retrieval pipelines from ingestion to grounded answers — including air-gapped, on-prem options.
Read more →№ 04Custom Model Fine-Tuning
SFT, RLHF/DPO alignment, and LoRA/QLoRA on open-source models — proprietary AI that you fully own.
Read more →№ 05AI Strategy & Research
Independent, research-backed advisory — readiness, technology selection, ROI cases, and governance.
Read more →LLM Integration & API Setup
Production-ready LLM integrations with cost controls, fallbacks, and observability built in.
Toolchain · OpenAI · Anthropic · Gemini · Mistral · LangSmith · Phoenix
We deliver production-ready LLM integrations — connecting OpenAI, Anthropic, Google Gemini, Mistral, and open-source models to your stack. Every integration includes prompt engineering, cost controls, rate-limit handling, fallback routing, and full observability so you know exactly how your AI is performing and what it is costing you. All integrations are framework-agnostic and can target any cloud or self-hosted endpoint.
| Deliverable | Scope & outcomes | Typical timeline |
|---|---|---|
| LLM API Quick-Start | Single-model integration with a basic prompt pipeline. API setup, environment configuration, prompt templates, and a working demo endpoint your team can build on. | 3–5 days |
| Multi-Model Integration | 2–4 model providers with intelligent routing, fallback handling, and cost-optimised dispatch. Resilience and cost management across OpenAI, Anthropic, and Gemini. | 1–2 weeks |
| Prompt Engineering Pack | Systematic prompt design using structured techniques (chain-of-thought, few-shot, role prompting). Includes an evaluation harness so you can measure and improve output quality. | 3–7 days |
| Cost & Latency Audit | Profile and optimise your existing LLM calls. We identify redundant requests, over-provisioned models, and prompt inefficiencies — delivering a written report with actionable fixes. | 2–4 days |
| LLM Observability Setup | End-to-end logging and tracing using LangSmith or Phoenix. Track token usage, latency, error rates, and prompt versions. Includes dashboards and alerting thresholds. | 1 week |
AI Agent Development
Single-task agents to coordinated multi-agent networks — with evals, audits, and human-in-the-loop controls.
Toolchain · LangGraph · CrewAI · AutoGen · Anthropic Agent SDK
We design and build autonomous AI agents — from single-capability task runners to coordinated multi-agent networks. Using LangGraph, CrewAI, AutoGen, and the Anthropic Agent SDK, we deliver agents with full evals, human-in-the-loop controls, and the audit trails your compliance teams require. All agents ship with fail-safes, rate limits, rollback mechanisms, runbooks, and monitoring setup.
| Deliverable | Scope & outcomes | Typical timeline |
|---|---|---|
| Single-Task Agent | An agent focused on one capability — research, email drafting, data extraction, or document classification. Includes tool integration, memory, and a working deployment. | 1–2 weeks |
| Multi-Tool Agent | An agent equipped with 3–6 tools (web search, database queries, API calls, file operations) connected through a planning loop with short-term memory and task tracking. | 2–4 weeks |
| Multi-Agent System | Coordinated network of 2–5 specialised agents with a supervisor/orchestrator layer. Each agent has a defined role, shared memory, and structured communication protocols. | 4–8 weeks |
| Agent Evaluation Suite | Automated evaluation framework: goal completion rate, hallucination detection, tool misuse, and adversarial probing. Includes red-team test library and reporting. | 1–2 weeks |
| Human-in-the-Loop Flow | Approval gates, interrupt mechanisms, and full audit logging for regulated or high-stakes workflows. Agents pause for human review at configurable checkpoints. | 2–3 weeks |
RAG & Knowledge Systems
End-to-end retrieval pipelines from ingestion to grounded answers — including air-gapped, on-prem options.
Toolchain · Pinecone · Weaviate · pgvector · Chroma · Qdrant · BGE · E5
End-to-end retrieval-augmented generation pipelines — from ingestion and chunking through embeddings, vector-store setup, hybrid search, and re-ranking, to grounded Q&A interfaces. We work with Pinecone, Weaviate, pgvector, Chroma, and Qdrant, and can deploy entirely on-premise for air-gapped environments.
| Deliverable | Scope & outcomes | Typical timeline |
|---|---|---|
| RAG Prototype | Single-corpus Q&A interface using a basic vector pipeline. Demonstrates the core retrieval loop and LLM answer generation. Ideal for stakeholder buy-in and early feasibility. | 3–5 days |
| Production RAG Pipeline | Full ingestion pipeline with document parsing, chunking strategies, embedding model selection, vector store deployment, hybrid (dense + sparse) search, and re-ranking. | 3–5 weeks |
| Multi-Source Knowledge Hub | Unified retrieval across PDFs, structured databases, web content, and APIs. Document routing, metadata filtering, access control per source, and a unified query interface. | 5–8 weeks |
| RAG Evaluation & Tuning | RAGAS-based evaluation of your existing RAG system: faithfulness, context recall, answer relevance, and retrieval precision. Written report with tuning recommendations. | 1–3 weeks |
| On-Premise / Private RAG | Fully air-gapped deployment with local embedding models (BGE, E5) and local LLMs (Ollama, vLLM). No data leaves your infrastructure. Includes hardware sizing guidance. | 6–10 weeks |
Custom Model Fine-Tuning
SFT, RLHF/DPO alignment, and LoRA/QLoRA on open-source models — proprietary AI that you fully own.
Toolchain · Llama 3 · Mistral · Phi-3 · LoRA · QLoRA · DPO · RLHF
Supervised fine-tuning (SFT), RLHF/DPO alignment, and LoRA/QLoRA parameter-efficient training on open-source models. We help you create proprietary AI models that understand your domain, your tone, and your customers — models that improve with every interaction and that you fully own. All fine-tuned models are delivered with full weights, training configs, and reproducibility instructions. We do not retain copies of your training data or model weights.
| Deliverable | Scope & outcomes | Typical timeline |
|---|---|---|
| Dataset Curation | Collection, cleaning, deduplication, and annotation pipeline for training data. Format standardisation, quality scoring, and a versioned dataset ready for fine-tuning. | 1–2 weeks |
| LoRA / QLoRA Fine-Tune | Parameter-efficient fine-tuning of an open-source base model (Llama 3, Mistral, Phi-3) for a single task or domain. Training, evaluation, and a merged or adapter model artefact. | 2–4 weeks |
| Full SFT Pipeline | End-to-end supervised fine-tuning: dataset preparation, training run, evaluation suite, and model deployment. Model card, benchmark results, and handover documentation. | 4–8 weeks |
| Alignment Tuning (DPO) | Direct preference optimisation using human or AI-generated preference pairs. Produces a model aligned to your values, tone, and safety requirements. | 3–6 weeks |
| Model Evaluation Report | Comprehensive benchmark assessment: task-specific metrics, comparison against baseline models, human evaluation results, and an error analysis with improvement recommendations. | 1–2 weeks |
AI Strategy & Research
Independent, research-backed advisory — readiness, technology selection, ROI cases, and governance.
Toolchain · Roadmaps · TCO modelling · EU AI Act · GDPR · Risk frameworks
Independent, research-backed advisory to help leadership invest in AI with evidence and clarity. We help you identify where AI creates real value in your business, select the right technologies, build the case for investment, and manage the risks — from first conversation to board-ready roadmap.
| Deliverable | Scope & outcomes | Typical timeline |
|---|---|---|
| AI Readiness Assessment | Structured review of your data infrastructure, team capabilities, existing tooling, and business processes. Identifies highest-value AI use cases and the gaps to address before building. | 1 week |
| Technology Selection | Model and platform evaluation across open-source and commercial options. A written recommendation with rationale, trade-off analysis, and total cost of ownership guidance. | 3–5 days |
| AI ROI Business Case | Cost-benefit model and executive presentation quantifying the financial and operational impact of your proposed AI initiative. Sensitivity analysis and risk-adjusted projections. | 1–2 weeks |
| Research Deep-Dive | State-of-the-art review on a chosen topic (multimodal models, synthetic data, agent safety). Delivered as a structured research report with practical implications for your context. | 1–3 weeks |
| AI Risk & Governance | Policy framework, bias audit methodology, compliance mapping (EU AI Act, GDPR), and an operational governance playbook tailored to your industry and risk appetite. | 2–3 weeks |
Three engagement structures, designed to combine.
All engagements begin with a scoping session at no cost, and we provide a written Statement of Work before any work begins.
| Engagement | Best for | How it works |
|---|---|---|
| Fixed-Price Project | Defined scope with clear deliverables and a known end date. | Deliverables, milestones, and acceptance criteria agreed upfront. Payment is milestone-linked. You review working software before each payment is triggered. |
| Monthly Retainer | Ongoing engineering capacity, priority support, or iterative development. | Guaranteed hours each month at a pre-agreed commitment level. Unused hours roll over once. Includes priority response SLA and regular strategy calls. |
| Hourly Advisory | Code reviews, technical guidance, workshops, or ad-hoc support. | Billed in minimum 1–2 hour blocks against confirmed hours. Invoiced bi-weekly. No retainer required — engage only when needed. |
| Hybrid | Projects that combine a build phase with ongoing support. | Discovery and build phases are fixed-price for predictability; post-launch support shifts to a retainer for sustained capacity at a blended rate. |
Guaranteed engineering capacity each month.
Retainers run alongside active fixed-price projects. Choose the tier that matches your priority response and engagement cadence.
- Email support
- Business-day SLA
- Monthly strategy call
- Ad-hoc task support
- Priority email & Slack
- 4-hr SLA (critical issues)
- Bi-weekly strategy calls
- Code reviews included
- Dedicated engineer
- 2-hr SLA · 24/5 coverage
- Weekly leadership sync