Generative AI Development Services
Put models that write, summarise and create to work.
Generative AI development is building products that create new text, images, code or summaries using models trained on large amounts of data. NetOfficials helps you pick the right use case, ground the model in your own content, add safety checks and ship it inside your product or workflow. Conversational products built on this are covered by our AI chatbot development services.
- Building software since
- 2011
- Approved before the build
- Written scope
- Tracked in Jira
- Sprint demos
- Confirmed in the contract
- Your IP
What Is Included in Our Generative AI Development Services
Getting a demo to work is quick. Making it dependable enough to ship is where the work lies.
Use Case Selection
We test which ideas a generative model can do well, and which need a different approach.
Retrieval from Your Content
The model draws on your documents and data, so answers are based on facts you control.
Prompt and Output Design
Instructions and output formats shaped so results are consistent and usable.
Guardrails and Review
Rules, filters and human review steps for what the model may say or do.
Evaluation
Test sets that score answers, so changes can be checked before release.
Product Integration
Generation built into your app, with usage limits and cost controls.
Who Our Generative AI Development Services Suit
Generative AI helps most where people spend hours writing, reading or searching. These are common starting points.
-
Content and marketing teams
Draft and adapt content faster
Produce first drafts, variations and summaries that an editor then approves.
- Brand voice guidelines in the prompts
- Editor approval before publishing
- Output saved to your tools
-
Knowledge heavy businesses
Search and summarise your documents
Let staff ask questions across contracts, manuals and reports and get sourced answers.
- Answers cite the source passage
- Access follows user permissions
- Works over your own files
-
Software product teams
Ship AI features to customers
Add drafting, summarising or assistant features to a product people already use.
- Cost and usage controls
- Evaluation before each release
- Fallbacks when the model fails
Ground the Model in Your Data or Fine Tune It?
There are two main ways to make a general model work for your business. You can feed it your content when it answers, or you can train it further on examples. We recommend an approach after testing both on your use case.
Grounding on your content is usually the better start when
- Answers must come from documents that change often
- You need to show the source of each answer
- You have few labelled examples of ideal output
- You want to start with the least setup
| Consideration | Ground on your content | Fine tune the model |
|---|---|---|
| Keeping up with new information | Update the documents | Needs retraining |
| Showing sources | Possible | Difficult |
| Data needed | Your documents | Many good examples of ideal output |
| Changing style or format | Helped by prompts | Stronger and more consistent |
| Effort to start | Lower | Higher |
| Best for | Q&A and summaries over company knowledge | A fixed tone, format or specialist task |
How We Deliver Your Generative AI Product
Every project moves from a written scope to agreed review points, so you always know what is happening and what comes next.
Frame the Use Case
What the model should produce, who checks it and what a good result looks like, in a scope you approve.
Signed scopePrototype and Test
A working prototype run against real examples to learn what the model does well and badly.
Tested prototypeBuild the System
Retrieval, prompts, guardrails and integration built in sprints, with a demo each time.
Working productEvaluate and Harden
Score answers on a test set, tighten safety rules and review risky cases.
Scored resultsRelease and Monitor
Launch with usage limits, then watch quality and cost and improve.
Live and measured
What Affects the Cost of Generative AI Development
We do not quote from a price list, because generative AI projects vary so much. Most of the cost comes from the points below, which we settle with you at the start.
Use case complexity
Summarising a page is simpler than drafting long, structured documents that must be accurate.
Your source content
Large, messy or fast changing document sets take more preparation to search well.
Safety and review needs
Stricter rules, approval steps and audit trails add design and build work.
Model usage fees
Hosted models charge by use, so volume and the model you pick shape the running cost.
Evaluation depth
More thorough test sets and human review cost more, and reduce the risk of poor output.
Integration
Connecting to your product, identity system and data stores adds build and test work.
Why Choose NetOfficials for Generative AI Development
The working terms below are part of every engagement, not extras.
One Cross-Functional Team
Design, development, testing and DevOps run through a single team, so decisions stay connected.
Senior From Day One
The senior engineer who scopes your work stays involved while it is delivered.
You Own Everything
IP assignment is confirmed in the contract, and you receive the full source code and documentation at handover.
Mutual NDA
We sign a mutual NDA before you share the details of your project.
Your Working Hours
We agree a communication window that suits your time zone at the start.
Visible Progress
Work is tracked in Jira, with a demo and your feedback at the end of every sprint.
How We Choose the Right Stack for Generative AI
We recommend technology for your project and your team's future, not for our convenience. Three questions guide every choice.
Will it handle your load?
We size the design for your expected users, data and growth, so it does not need rebuilding when the business grows.
Will it fit your systems?
We check how the stack connects with the tools, databases and partners you already rely on before we commit to it.
Can you support it later?
We favour well-supported technology with a wide talent pool, so your own team or any future partner can maintain it.
Technologies we work with
- LLM APIs
Python
TensorFlow
Node.js
TypeScript
Go
PostgreSQL
MongoDB
GraphQL
AWS
Azure
Google Cloud
Docker
KubernetesFlexible Ways to Work With Us
Pick the model that fits where your project is today. You can move between them as it grows.
Project Delivery
We deliver an agreed scope, timeline and price.
- Written discovery scope
- Milestone reviews
- Best for a clear first release
- For ongoing work
Dedicated Team
A team that works only on your project, led by a named delivery lead.
- Cross-functional team
- Sprint demos and planning in Jira
- Scale the team up or down
Team Extension
Specialists who join your existing team, tools and processes.
- Works in your process and tools
- Agreed overlap hours
- Best for adding a missing skill
More From Our AI Development Practice
This service is part of our ai development practice. These pages cover the services most often needed alongside it.
Generative AI Development FAQs
What is generative AI?
Generative AI is software that creates new content, such as text, images or code, from a prompt. It does this using models trained on very large collections of examples.
Can generative AI use our private data?
Yes. We can connect a model to your documents so it answers from them. We agree in discovery which data it may see and who may ask.
How do you deal with made up answers?
We ground the model in your content, ask it to cite sources, add checks for answers it cannot support and test on a scored set of questions. Some uses also need human review.
Which model will you use?
We recommend a model after testing options against your use case, looking at quality, cost, speed and where your data may be processed.
Do I need to fine tune a model?
Often not at the start. Grounding a general model in your content is usually quicker, and we test whether fine tuning would add enough to justify it.
Who owns what you build?
You do. Ownership is settled in the contract before the build begins, and handover includes the full source code and documentation.
Ready to Build with Generative AI?
Share a few details. A senior engineer reads every brief and replies with questions and a suggested next step.
- We review your briefAnd reply with questions
- We sign a mutual NDABefore you share the details
- You get a written scopeTeam, timeline and model, before any commitment