AI Engineering
Generative AI Development
Turn large language models into reliable product features that write first drafts, condense long documents and pull structured facts from unstructured text, all in your organisation’s voice.
Introduction
From impressive demo to dependable feature
Generative AI can produce fluent text in seconds, but fluency is not the same as usefulness. A proposal draft that invents product features, or a summary that drops the one clause legal cares about, creates more work than it saves.
Our generative AI development focuses on the conditions that make output trustworthy: the right source material, clear instructions, structured outputs your software can validate, and review steps in the places where mistakes would be costly.
Business challenges
The problems this solves
Inconsistent output quality
The same prompt can produce excellent results one day and unusable ones the next, which makes teams hesitant to rely on it.
Hallucinated facts
Models fill gaps with plausible-sounding details unless they are grounded in verified sources and told how to handle missing information.
Brand and compliance risk
Generated content can drift from approved terminology, tone or regulatory wording if guardrails are not designed in.
Unclear cost at scale
Token usage that looks trivial in a pilot can become significant once thousands of users rely on the feature daily.
Proposed approach
How we approach it
We treat each generative feature as a small, testable system rather than a clever prompt.
- Define the job precisely: the input, the expected output format and what a reviewer would reject
- Ground generation in retrieved documents, product data or templates instead of the model’s memory
- Request structured output (JSON or field-by-field) so software can validate it before display
- Build an evaluation set from real examples and score every prompt or model change against it
- Choose the smallest model that meets the quality bar, and cache repeated work to control cost
Core capabilities
What we deliver
Drafting assistants
First drafts of proposals, product descriptions, job adverts and customer replies, based on approved templates and data.
Summarisation
Concise summaries of contracts, reports, meeting transcripts and case histories, tuned to what each audience needs.
Information extraction
Turning emails, PDFs and free-text notes into structured fields that downstream systems can use.
Translation and localisation
Context-aware translation that preserves terminology lists and formatting for multilingual teams and customers.
Semantic search
Finding content by meaning rather than exact keywords across knowledge bases and document stores.
Content transformation
Rewriting material for different channels, reading levels or formats while keeping key facts intact.
Use cases
Relevant use cases
Examples of where this capability is commonly applied.
Sales proposal first drafts
Pull client details from the CRM and approved service descriptions into a structured proposal draft that a salesperson refines rather than writes from scratch.
Contract clause summaries
Highlight renewal dates, liability caps and termination terms from supplier agreements for procurement review.
Product catalogue enrichment
Generate consistent descriptions and attribute tags for thousands of SKUs from supplier specifications.
Support case condensation
Give agents a short summary of a long ticket history before they respond, so customers do not have to repeat themselves.
Technology considerations
Choosing the right technology
Model choice depends on quality requirements, data sensitivity, latency and budget. We stay provider-neutral and design so that models can be swapped as the market evolves.
Model access
- Commercial LLM APIs
- Open-weight models
- Private cloud hosting
Grounding
- Vector search
- Hybrid keyword + semantic search
- Prompt templates
Engineering
- Python
- Node.js
- Structured output validation
- Evaluation harnesses
Implementation
Implementation process
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1
Use-case definition
Document inputs, outputs, reviewers and success criteria with the people who will use the feature.
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2
Data and template audit
Collect example inputs, approved wording and reference outputs to build the evaluation set.
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3
Prototype and evaluate
Compare prompting strategies and models against the evaluation set, not against intuition.
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4
Integrate and harden
Embed the feature in your application with validation, fallbacks, logging and rate limits.
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5
Pilot and tune
Release to a small group, collect structured feedback and refine before wider rollout.
Explore more
Related services and solutions
AI Agent Development
Goal-oriented AI agents that plan tasks, use your tools and escalate to people when needed.
Learn more about AI Agent DevelopmentIntelligent Document Processing
Extract, validate and route data from invoices, forms and contracts automatically.
Learn more about Intelligent Document ProcessingAI Knowledge Management
Retrieval-augmented assistants that answer questions from your documents with citations.
Learn more about AI Knowledge ManagementAPI Development & Integration
Well-designed APIs and dependable integrations that keep your systems and data in sync.
Learn more about API Development & IntegrationFAQs
Frequently asked questions
Answers to the questions we hear most often. If yours is not covered here, our team is happy to talk it through.
In our experience it changes their work more than it replaces it. People spend less time on blank-page drafting and repetitive summarising and more time on judgement, editing and client conversations.
We ground answers in retrieved source material, instruct the model to say when information is missing, validate structured outputs, and measure factual accuracy on an evaluation set before release.
Yes. Style guides, approved examples and terminology lists are included in the instructions, and we check tone as part of evaluation. For high volumes, fine-tuning on reviewed examples can further improve consistency.
A focused proof of value on one workflow, using a sample of your real documents, followed by integration into the tool your team already uses. Scope and timing depend on data access and review requirements, which we confirm during discovery.
Have a document-heavy process in mind?
Share an example workflow with us and we will outline where generative AI could help, and where it should not.
