Leadership without an AI lead
You have budget and board interest but no one senior who has shipped LLM systems. We supply that judgement part-time or project-by-project.
Fractional AI CTO
AI Services
AI consulting and implementation that reaches production
Strategy, secure build and measurable operation from one senior team. We pick the use cases worth doing, ship them with Claude, GPT, Gemini or open-weight models, and keep them honest in production.
- Shipping software since 2006
- 5 global offices, 18+ time zones
- Senior people, small teams, fixed scopes
Who this is for
For teams that need AI to work, not just demo
Most of our AI clients have already tried something. A chatbot that answers confidently and wrongly. A pilot that nobody could cost. A vendor tool that cannot reach the systems that matter. We fix the gap between a promising prototype and a system your business can rely on.
Engineering teams with a backlog of AI asks
Your developers are capable but new to retrieval, agents and evals. We build alongside them and leave the patterns behind.
AI implementationRegulated or data-sensitive businesses
Healthcare, finance, legal and public sector teams who need AI that passes security review and survives an audit.
AI security & governance
The catalogue
Fifteen AI services, five stages
Every service below is a standalone engagement with its own deliverables and timeline. Most clients combine two or three.
1. Strategy and leadership
AI Strategy & Consulting
Score your use cases on value and feasibility, agree a 12-month roadmap and a funded first project.
ExploreFractional AI CTO
Senior AI leadership on a part-time retainer: architecture, vendor choices, hiring and governance.
ExploreAI Implementation
From a scoped pilot to a production system with owners, runbooks and measured outcomes.
Explore2. Build
LLM App Development
Production applications on Claude, GPT, Gemini or open-weight models, with routing, tools and evals built in.
ExploreAI Agents & Automation
Agents that plan, act through tools and verify their own work inside guardrails you set.
ExploreMCP Server Development
Model Context Protocol servers that expose your CRM, database and ticketing to any AI host safely.
ExploreAI Chatbots
Support and sales assistants grounded in your content, with deflection, escalation and measurement.
ExploreVoice AI
Phone and in-app voice agents built on a speech-to-text, LLM and text-to-speech pipeline with a latency budget.
ExploreAI for Mobile Apps
On-device and cloud AI features in native iOS and Android apps, from a team that has built 1500+ apps.
Explore3. Knowledge, data and perception
AI Data Engineering
Ingestion, cleaning, chunking, embeddings and feature pipelines that make everything else possible.
ExploreComputer Vision
Document, image and video understanding with multimodal models and classic vision pipelines.
ExploreSpeech-to-Text
Transcription, diarisation and summarisation, proven in our Eventify session transcription product.
ExploreAI Knowledge Base (RAG)
Answers grounded in your documents, with citations and access control. A dedicated practice.
Visit the knowledge base sectionVector Search
Semantic and hybrid search on pgvector, Milvus, Pinecone, Qdrant or Weaviate. A dedicated practice.
Visit the vector search sectionAI Integration
Wire models into Salesforce, ERPs, ticketing and legacy systems through APIs, events and MCP.
Explore4. Secure and govern
LLM Security Audit
OWASP LLM Top 10 coverage of a production application, with fixes prioritised by exploitability.
Visit AI securityPrompt Injection Defence
Layered defences for agents and RAG systems that read untrusted content.
Visit AI securityAI Governance & EU AI Act
Policies, controls and evidence for the EU AI Act and ISO 42001.
Visit AI security5. Operate and improve
Fine-tuning & Evals
Measure quality before you change anything, then decide between prompting, retrieval and fine-tuning.
ExploreLLMOps & Monitoring
Tracing, cost and latency dashboards, quality drift alerts and release gates for LLM features.
ExploreLLM Cost Calculator
Free tool: estimate monthly model spend from traffic, prompt size and model mix before you commit.
Open the calculator
How an engagement runs
Scope, prove, build, secure, operate
The same five stages apply whether the project is a two-week pilot or a year-long programme. Each stage ends with something you can hold us to.
Scope
A written problem statement, success metric, data inventory and risk register. Usually one to two weeks.
Prove
A thin, working slice on real data with an eval set, not slides. Two to six weeks depending on data access.
Build
Production architecture, integrations, guardrails and a release gate tied to the eval scores.
Secure
Threat model, prompt injection testing, data-handling review and governance evidence before launch.
Operate
Monitoring for cost, latency and quality drift, with a monthly review that feeds the next use case.
What you get in week one
A named senior lead, a shared workspace, a written scope with a success metric, an inventory of the data and systems involved, a first eval set drafted from real examples, and a risk register that already names the two or three things most likely to go wrong. If we cannot produce these in a week, the project is not ready and we will say so.
Why it works
Three habits that separate production AI from demos
Evals before features
We write the test set before the prompt. Every change to a model, prompt or retrieval index is scored against it. Opinions stop deciding releases. See fine-tuning and evals.
Security as a build step
Prompt injection, data leakage and over-privileged tools are design problems, not launch-day surprises. Our secure RAG architecture patterns are baked in.
Costs you can forecast
Model routing, caching and context budgets are designed up front. We publish the unit economics with the first pilot and track them in LLMOps monitoring.
Go deeper
Related practices and reading
Practice
AI Security & Governance
Audits, red teaming, prompt injection defence, EU AI Act and ISO 42001 readiness.
Visit PracticeAI Knowledge Base
Enterprise RAG, document Q&A, support and internal copilots.
Visit PracticeVector Search
Embeddings strategy, hybrid search and vector database selection.
Visit Free toolAI Readiness Assessment
Ten minutes, a score and a list of what to fix before you spend on a build.
Start GuideThe AI CTO Playbook
What a technical AI leader should do in the first 90 days, whether in-house or fractional.
Read ProofCase Studies
Eventify's AI support bot, session speech-to-text and attendee matchmaking, built and operated by us.
See the work
FAQ
Frequently asked questions
Which AI service should we start with?
If you have a shortlist of ideas but no agreed priority, start with AI strategy. If you already know the use case, go straight to AI implementation. If you are unsure whether your data and team are ready, take the free AI readiness assessment first.
Which models and platforms do you work with?
Anthropic Claude (API, MCP and Claude Code), OpenAI, Google Gemini and open-weight models such as Llama and Mistral. We run them on Cloudflare, Azure or your own infrastructure, with PostgreSQL + pgvector or Milvus for retrieval.
Do you only consult, or do you build as well?
Both. Teksmobile has shipped software since 2006, and our AI practice is delivered by the same senior engineers. Strategy work ends in a backlog we are willing to build ourselves, which keeps the advice honest.
How do you price AI engagements?
Fixed scope where the outcome can be defined up front, such as audits, pilots and assessments. Time-and-materials or a retained team for longer build and run phases. See our engagement models for the options.
Can you work with our existing security and compliance requirements?
Yes. Every build goes through our AI security practice: threat modelling, prompt injection defences, data handling rules and, where needed, EU AI Act and ISO 42001 alignment.