We help companies put AI to work in real business processes, from knowledge access to repetitive decisions. Our engineers connect models to trusted data, build review points and make the results useful to the people doing the work.
Tranz / Engineering in practiceAn AI feature needs a useful task, dependable information and a clear way to handle uncertain results. We start with the workflow and define what the model can decide, what a person must review and how quality will be checked.
We help product teams add generative AI where it improves a clear user task. Our engineers design prompts, data access, evaluation and human review so the feature fits your product and its operating rules.
Explore this service ↗We help companies make approved knowledge easier to find and use through retrieval-augmented generation. Our engineers structure sources, respect access permissions and test whether answers point back to the right information.
Explore this service ↗We help teams automate multi-step work that consumes time and attention. Our engineers define tool access, checkpoints and escalation paths so agents can assist without hiding decisions or exceptions.
Explore this service ↗We help companies bring AI into the applications and workflows they already use. Our engineers connect models, data and APIs while keeping permissions, reliability and human oversight in view.
Explore this service ↗These are examples of workflows we can scope with your team.
Help teams find answers in approved policies, product material and internal documentation, with references they can check.
Extract and organise information from incoming documents, then route incomplete or uncertain records for review.
Draft responses, summarise records and prepare the next action inside an existing business workflow.
We agree on the deliverables for your project before delivery begins.
Prioritised workflows, dependencies, responsibilities and acceptance criteria for the agreed release.
Designs, application modules or integrations matched to the selected services, with regular review points.
The documentation, configuration, operating guidance and team walkthroughs included in your delivery plan.
These are technology options for the engagement. We choose the stack around your existing systems, deployment needs and team; each project uses a relevant subset.
Python
Java
Hugging Face
Pinecone
QdrantWeaviate
MilvusChroma
PostgreSQL + pgvector
LangChain
LangGraph
Spring AI
LangSmith
n8nWe evaluate model families with representative examples from your product. The selected model and version depend on answer quality, tool use, response time, cost and deployment requirements.
GPT model family
Evaluate GPT models for product assistants, structured responses and workflows that call application tools.
Explore GPT models
Opus · Sonnet · Haiku
Compare Claude options for document analysis, conversational assistance and multi-step tool workflows.
Explore Claude models
Pro · Flash · Flash-Lite
Evaluate Gemini models for text and multimodal experiences, matching response quality and latency to the task.
Explore Gemini models
Large · Small · Codestral
Compare Mistral models for language and coding workflows, with hosted or deployable options where the chosen model supports them.
Explore Mistral models
Llama model family
Assess Llama models when deployment control matters, including hosting capacity, model licensing and operational ownership.
Explore Llama models
Qwen model family
Evaluate Qwen options for language, coding and multimodal tasks against your data, deployment needs and acceptance criteria.
Explore Qwen models
DeepSeek model family
Assess DeepSeek models for reasoning and application assistance, testing reliability, tool behaviour and operating cost on your workflow.
Explore DeepSeek modelsWe test the proposed model with your prompts, retrieved context and application tools, then agree on the version, review points and fallback behaviour. For RAG, we also evaluate retrieval and embedding choices; for agents, we test action boundaries and recovery when a tool fails.
Choose a workflow, representative inputs and the decisions that require human approval.
Review source quality, permissions, retrieval rules and connections to existing systems.
Compare outputs against agreed examples; test missing evidence, incorrect answers and tool failures.
Introduce the feature to a defined user group and track quality, latency, usage and operating cost.
These portfolio examples provide context for the products and business processes we work on.
One connected UK pharmacy product spanning stock, delivery, prescribing, coding and more.
ExploreAn everyday money app for expenses, budgets, savings goals and shared splits.
ExploreA connected commerce and distributor operations platform.
ExploreWe agree on representative examples and acceptance criteria before the pilot. Reviews cover answer quality, evidence, failure handling and the effort required from users.
We first review who can access each source, what information can leave your systems and the deployment requirements. Those decisions shape model access, retrieval and logging.
Review points depend on the action. Drafting an internal summary and changing an operational record require different controls; we define those boundaries in the workflow.
Share your current workflow, existing systems and the result you want. We’ll help define a practical next step.
