Generative AI development services, grounded in your own data
We build applications that draft, summarise, extract and answer from your company's content - showing the sources behind every output and routing anything consequential to a person before it leaves the building.
- sources
- Your approved content
- output
- Cited and checked
- release
- A person approves
- Approved sources3 retrieved
- Knowledge source: Service agreement v3section 4.2
- Knowledge source: Support catalogueweekend cover
- Knowledge source: Framework contractpricing annex
- AI model: Draft generatedproposal template v2 · cites every figure- drafting
- Rule check: Terms checked against sourceprices and dates match- pass
- ReviewPerson decides: Account manager accepts or edits2 suggestions applied · 1 open
01 / Where generative AI fits
Five kinds of work generative models do well
Generative AI is useful wherever people turn a lot of text into a smaller, structured or better-written result. It is weak where exact calculation, assured facts or long chains of decisions are needed - those parts stay with code and people.
Definition · Generative AI application development
Building software in which a generative model - usually a large language model - produces text or structured data as one step of a business task, together with the retrieval, checks, review flow and interface that make the result trustworthy.
- 01
Drafting
Proposals, replies, reports and descriptions written from your templates and records, ready for a person to edit and approve.
- 02
Summarising
Long cases, contracts, call transcripts or ticket histories reduced to what the next person needs to know, with links back to the passages used.
- 03
Extracting
Fields pulled from PDFs, emails and scans into a defined structure your systems can store - with a confidence level for each value.
- 04
Classifying and routing
Incoming items sorted by type, urgency or owner, including free-text requests that fixed keyword rules handle badly.
- 05
Searching and answering
Questions answered from your documents in plain language, with the sources shown so readers can check them.
02 / Generative AI solutions
Generative AI applications we build
Our generative AI services produce product-quality applications, not a prompt in a shared chat tool. Each one has users, permissions, an interface built for the task, and tests.
- G1
Document drafting assistants
Proposals, quotes, reports and letters assembled from approved templates, clauses and records. The user edits and approves; the system records which sources were used.
- G2
Knowledge search with citations
Plain-language questions answered from policies, manuals, contracts and records, limited to what each user is allowed to read.
- G3
Document extraction into your systems
Invoices, forms, certificates and correspondence turned into structured records, validated against your data and handed to a person when unsure.
- G4
Summaries and reporting
Ticket histories, meeting notes, call transcripts and weekly activity condensed into briefs and reports in a consistent format.
- G5
Content transformation
Rewriting existing content for another audience, format, tone or language - product descriptions, help articles, internal notices - under your style rules.
- G6
Copilots inside your software
Assistance on the screen where work happens. For conversational assistants and copilots in depth, see AI chatbot and copilot development.
Chains show the typical mechanism of each application; examples on this page use fictional data.
03 / Reliability
How generative output becomes reliable enough for real work
Three mechanisms, applied together. None of them is optional for an application whose output reaches customers or records.
- 1 · ground
Use only approved sources
- Knowledge source: Pricing policy 2026current version- used
- Knowledge source: Pricing policy 2024superseded- excluded
The application retrieves the relevant passages from your current documents and records, and the model is instructed to answer only from them. Retrieval architecture is covered under AI integration.
- 2 · constrain
Generate into a defined shape
- Rule check: Formatproposal v2 template
- Rule check: Length≤ 400 words
- Rule check: Requiredprice, term, scope
- Rule check: Citationsfor every figure
Templates, output schemas and explicit rules define what a valid result looks like, so output can be checked automatically before anyone reads it.
- 3 · check
Test, then review
- Summary of a 30-page contractmentions all termination clausespass
- Question outside the documentssays it cannot answerpass
- Invoice with two totalsflags for review, no guesspass
- Policy changed last weekuses the newest versionreview
- Request in Spanishanswers in Spanish from English sourcespass
An evaluation set of real examples is run before release and after every change to prompts or models; consequential outputs also go to a person.
04 / Choosing the approach
Prompting, retrieval, fine-tuning or a self-hosted model
Most projects start with the first two rows. The others are justified by measurements, not by default.
| Approach | What it is | Good fit when… | Trade-offs |
|---|---|---|---|
| Prompting a hosted model | A capable model from a major provider, instructed with your rules and examples. | The task needs general language skill and little company knowledge. | Fast to start; quality depends on instructions; data leaves your environment under the provider's terms. |
| Adding retrieval (RAG) | The application searches your documents and gives the model only the relevant passages. | Answers must rest on your current content and show sources. | Needs well-maintained sources and access filtering; the usual default for business use. |
| Fine-tuning a model | A model further trained on your examples to fix a format, style or narrow task. | High volume of one task, a strict format, or a smaller model to cut cost. | Needs good training examples and re-evaluation; does not add up-to-date knowledge. |
| Self-hosted open-weight model | A model with published weights running in your cloud account or data centre. | Data must not leave your environment, or volume makes hosting cheaper. | Infrastructure to run and update; usually less capable than the strongest hosted models. |
05 / Controls
Controls built into every generative AI solution
These are configuration and code in the application, documented for your security and compliance teams.
Source restrictions
- Approved collections only
- Superseded versions excluded
Personal data handling
- Redaction before model calls where not needed
- Retention set per data type
Output validation
- Schemas and required fields
- Blocked phrases and claims
Review queues
- Approval before sending or saving
- Edits captured as feedback
Logging
- Inputs, sources and outputs per request
- Searchable for audits
Cost limits
- Budgets per user or workflow
- Model choice per step
06 / How a project runs
From a use case to a monitored generative AI application
In our generative AI development services, the evaluation set is written before the prototype, so "good enough" is defined by your team rather than by how impressive a demo looks.
- stage
Use-case & data review
The task, the sources it needs, who reviews output and what a good result looks like.
out →Scope and quality criteria
- stage
Evaluation set
Real examples with expected results, agreed with the people who do the work today.
out →Test set you keep
- stage
Prototype
Retrieval, prompts and model options compared on the evaluation set; cost per task measured.
out →Scores, model choice, cost
- stage
Production build
Interface, integrations, review flow, logging and controls, delivered in short cycles.
out →Tested application
- stage
Monitored launch
Pilot with one team, review of live outputs, then wider rollout and handover.
out →Live system, runbook
07 / Budget
What drives the cost of generative AI development
Build cost and running cost are estimated separately. Running cost is measured in the prototype, per task, before anything is rolled out.
When you compare generative AI development companies, ask each one for both figures - and for the test set they will use to show the quality you are paying for.
Source preparation
How many collections, how current they are, and whether scanned or messy material needs work first.
Quality bar
Stricter accuracy needs larger test sets, more validation and review flows.
Integrations
Where inputs come from and where results are saved: CRM, document storage, ERP, email.
Volume and length
Model usage is billed by the amount of text processed, so volume drives running cost.
08 / Questions
Frequently asked questions
What are generative AI development services?
They cover designing and building software in which a generative model - usually a large language model - produces text or structured data as part of a business task: drafting documents, summarising cases, extracting fields from files or answering questions from a knowledge base. The work includes the data connections, quality checks, review steps and interfaces around the model, not only the prompt.
How do you stop a generative AI application from making things up?
By limiting what it may rely on and checking what it produces. The application retrieves approved sources and the model is instructed to answer only from them, with citations. Outputs that must follow a format are validated against a schema. An evaluation set of real examples is run before release and after every change, and outputs with business consequences go to a person for approval. This reduces errors substantially; it does not make them impossible, which is why the review step exists.
Which generative AI model is best for our use case?
There is no single answer, and the ranking changes often. We shortlist models by the constraints that matter to you - data location, cost per request, response time, language and quality on your task - and compare them on your own evaluation set. The application keeps the model behind one interface so you can switch when a better option appears.
Do we need to fine-tune a model?
Usually not at first. Retrieval of your documents and precise instructions solve most tasks. Fine-tuning is worth testing when you need a very consistent output style or format at volume, or want a smaller model to handle a narrow task more cheaply. We decide this with measurements, not by default.
Will our documents be used to train someone else's model?
They should not be, and we design for that. We use model deployments whose terms exclude training on your requests, or keep processing within your cloud account or infrastructure when required. The data flow is documented so your security team can review it. Deployment options are described on the AI integration page.
What does a generative AI application cost to run?
Running cost is mostly model usage, billed by the amount of text processed, plus hosting for search indexes and the application. It scales with volume and document length. We measure cost per task during the prototype and design to a budget - for example by using a smaller model for routine steps and a larger one only where quality requires it.
What is the difference between generative AI and agentic AI?
Generative AI produces content: a draft, a summary, extracted data. Agentic AI uses a model to decide on and carry out steps - calling tools, reading and updating systems - towards a goal. Many products combine both. If the software needs to act across systems, see AI agent development.
Start node · your brief
Have documents and text work that a model could take on?
Describe two or three typical inputs and the result you expect from each. That is enough to discuss whether generative AI is the right tool and what a first version would include.