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AI Engineering

RAG Development

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.

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Conceptual engineering illustration for RAG DevelopmentTranz / 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.

01

Source preparation

Review documents, metadata and update cycles so retrieval uses information your team can trust.

What you receive

Source inventory, ingestion rules and indexing plan.

02

Permission-aware retrieval

Retrieve relevant material within user access rules and attach source references to the answer.

What you receive

Retrieval pipeline, access checks and citation behaviour.

03

Retrieval evaluation

Test coverage, missing information and conflicting sources before exposing answers to users.

What you receive

Question set, retrieval checks and refresh procedures.

Where it helps

Start with a real task, then shape the solution

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

01

Knowledge assistants

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

02

Document workflows

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

03

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

Source inventory, ingestion rules and indexing plan; Retrieval pipeline, access checks and citation behaviour; Question set, retrieval checks and refresh procedures.

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

PythonJava

Model ecosystem

Hugging Face

Vector databases & search

PineconeQdrantWeaviateMilvusChromaPostgreSQL + pgvector

Retrieval frameworks

LangChainSpring AI

AI tracing & evaluation

LangSmith
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RAG architecture

How the RAG pipeline connects knowledge to an answer

Two connected flows prepare your knowledge base and retrieve relevant evidence when someone asks a question. The embedding model used for queries must be compatible with the one used to index the documents.

Retrieval-augmented generation pipelineIndexing: approved sources are cleaned, split into chunks and embedded, then stored in a vector database with source references and permissions. At query time: a user question is embedded, a retriever reads the same database using access filters, context is selected and added to the prompt, and an LLM returns an answer with citations or reports missing evidence. Evaluation and monitoring cover both flows.01 / KNOWLEDGE INDEXINGApproved sourcesDocuments, records, APIsLoad & cleanParse and preserve metadataSplit into chunksKeep source referencesCreate embeddingsEncode each passageVector databaseStore vectors + permissionsRetriever reads the indexed knowledge base02 / QUESTION TO GROUNDED ANSWERUser questionIdentity + access scopeEmbed queryCompatible embedding modelRetrieveSimilarity + access filtersSelect contextOptional rerankingCompose promptQuestion + source passagesLLM generationAnswer from the contextAnswer + citationsOr report missing evidenceQuality and operations across both flowsSource refresh & deletion · Retrieval evaluation · Answer grounding · Tracing · Latency & cost · Human review

01 / Knowledge indexing

  1. Approved sourcesDocuments, records, APIs
  2. Load & cleanParse and preserve metadata
  3. Split into chunksKeep source references
  4. Create embeddingsEncode each passage
  5. Vector databaseStore vectors + permissions

↓ The retriever below reads this vector database.

02 / Question to grounded answer

  1. User questionIdentity + access scope
  2. Embed queryCompatible embedding model
  3. RetrieveSimilarity + access filters
  4. Select contextOptional reranking
  5. Compose promptQuestion + source passages
  6. LLM generationAnswer from the context
  7. Answer + citationsOr report missing evidence

Across both flows: source refresh, retrieval evaluation, answer grounding, tracing, latency, cost and human review.

Illustrative architecture. Permissions are enforced during retrieval; source updates and deletions feed back into the index. When evidence is insufficient, the application should say so or route the question for review.
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.

Explore Claude models
Google

Gemini

Pro · Flash · Flash-Lite

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

Explore Gemini models
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.

Explore Mistral models
Meta

Llama

Llama model family

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

Explore Llama models
Alibaba / Qwen

Qwen

Qwen model family

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

Explore Qwen models
DeepSeek

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

01

Define the task

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

02

Prepare the data

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

03

Evaluate a pilot

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

04

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.

01

Pills Pharmacy Platform (Pills ERP)

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

Explore
02

Beewise

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

Explore
03

TranzIndia Direct Selling Software

A connected commerce and distributor operations platform.

Explore
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 rag 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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