AI Engineering

Generative AI Development

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.

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Conceptual engineering illustration for Generative AI Development

Tranz / Engineering in practice

What we can help with

AI that fits the work your team needs to do

An 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.

Product assistants

Design conversational or inline assistance for a specific task, including how users check and correct the result.

What you receive

Interaction flows, prompt templates and response formats.

Content and document generation

Generate drafts from approved context and preserve a review step before publishing or updating records.

What you receive

Draft workflows, validation rules and review states.

Evaluation and release controls

Compare model outputs on representative inputs and define handling for uncertain, unsafe or unavailable responses.

What you receive

Evaluation examples, acceptance criteria and fallback paths.

Where it helps

Start with a real task, then shape the solution

These are examples of workflows we can scope with your team.

Knowledge assistants

Help teams find answers in approved policies, product material and internal documentation, with references they can check.

Document workflows

Extract and organise information from incoming documents, then route incomplete or uncertain records for review.

Assisted operations

Draft responses, summarise records and prepare the next action inside an existing business workflow.

A concrete scope

Know what the engagement will produce

We agree on the deliverables for your project before delivery begins.

A defined scope

Prioritised workflows, dependencies, responsibilities and acceptance criteria for the agreed release.

Reviewable work

Interaction flows, prompt templates and response formats; Draft workflows, validation rules and review states; Evaluation examples, acceptance criteria and fallback paths.

A practical handover

The documentation, configuration, operating guidance and team walkthroughs included in your delivery plan.

Frameworks & tools

A toolkit shaped around your requirements

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.

AI development languages

Python

Java

Model ecosystem

Hugging Face

AI application frameworks

LangChain

Spring AI

Vector databases & search

Pinecone

Qdrant

PostgreSQL + pgvector

AI tracing & evaluation

LangSmith

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LLM models

Match the model to the task

We 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.

OpenAI

GPT

GPT model family

Evaluate GPT models for product assistants, structured responses and workflows that call application tools.

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Anthropic

Claude

Opus · Sonnet · Haiku

Compare Claude options for document analysis, conversational assistance and multi-step tool workflows.

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Google

Gemini

Pro · Flash · Flash-Lite

Evaluate Gemini models for text and multimodal experiences, matching response quality and latency to the task.

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Mistral AI

Mistral

Large · Small · Codestral

Compare Mistral models for language and coding workflows, with hosted or deployable options where the chosen model supports them.

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Llama

Llama model family

Assess Llama models when deployment control matters, including hosting capacity, model licensing and operational ownership.

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Alibaba / Qwen

Qwen

Qwen model family

Evaluate Qwen options for language, coding and multimodal tasks against your data, deployment needs and acceptance criteria.

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DeepSeek

DeepSeek model family

Assess DeepSeek models for reasoning and application assistance, testing reliability, tool behaviour and operating cost on your workflow.

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Model selection is part of the engineering work

We 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.

How we work

Move from a clear brief to a useful release

Define the task

Choose a workflow, representative inputs and the decisions that require human approval.

Prepare the data

Review source quality, permissions, retrieval rules and connections to existing systems.

Evaluate a pilot

Compare outputs against agreed examples; test missing evidence, incorrect answers and tool failures.

Release and monitor

Introduce the feature to a defined user group and track quality, latency, usage and operating cost.

Relevant projects

Explore the workflows behind our work

These portfolio examples provide context for the products and business processes we work on.

Pills Pharmacy Platform (Pills ERP)

One connected UK pharmacy product spanning stock, delivery, prescribing, coding and more.

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Beewise

An everyday money app for expenses, budgets, savings goals and shared splits.

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TranzIndia Direct Selling Software

A connected commerce and distributor operations platform.

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Before we begin

Questions about the engagement

How do you check whether the AI is useful?

We agree on representative examples and acceptance criteria before the pilot. Reviews cover answer quality, evidence, failure handling and the effort required from users.

Can AI use private business information?

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.

Will people still review the results?

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.

Discuss generative ai development

Tell us what needs to work better.

Share your current workflow, existing systems and the result you want. We’ll help define a practical next step.

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