Generative AI development services built for real work, not demos
We design and build generative AI applications that draft, summarise, extract and create inside your business, with quality checks, brand controls and clear IP ownership. Senior engineers, fixed prices, and code that lives in your repository.
What are generative AI development services?
Generative AI development services design, build and run software that uses large language models and related models to create new output: drafts, documents, summaries, structured data, images or code. A generative AI development company handles the whole path, from choosing the use case and model to grounding output in your data, adding quality and brand checks, integrating with your systems and deploying securely. Vrisic builds these applications for companies in the US, UK and UAE, and you own the result.
| What we build | Drafting copilots, document generators, summarisers, extraction pipelines and content systems |
|---|---|
| First release | Focused copilot in 4 to 8 weeks |
| Typical budget | $15,000 to $120,000; full products from $120,000 |
| Models | GPT 5 family, Claude, Gemini, Llama and Mistral, chosen on your test cases |
| Quality control | Templates, grounding, automated evals and human review |
| Ownership | You own the code, prompts, templates and evaluation data |
Generative AI development for real work, not demos, explained
- 1Ground the model in your templates and data
- 2Generate the first draft
- 3Check brand, prices and terms automatically
- 4A person reviews and sends
Read the video transcript
Generative AI development for real work, not demos. Drafting, summarising and extraction with quality checks built in. Here is the problem. Proposals and reports take days to write. Generic AI tools get facts and prices wrong. And unclear who owns what the model produces. Here is how it works. First, ground the model in your templates and data. Second, generate the first draft. Third, check brand, prices and terms automatically. And finally, a person reviews and sends. What do you get? Clear IP terms: the output and the code are yours. Evaluation suite on your real examples. Model choice you can change later. Focused copilot $15,000 to $45,000, in 4 to 8 weeks. The easiest way to start is a $1,500 AI Pilot on your own data. You see a working version in ten business days, and the fee is credited to the full build. Book a free call, or message us on WhatsApp, at vrisic.com.
Generative AI applications we build
Most generative AI application development falls into seven patterns. Naming the pattern early sets the quality controls, the model choice and the budget.
- 01
Drafting copilots
An assistant beside the person doing the work, producing a first draft of an email, proposal, report, claim letter or care plan from the case in front of them. The person edits and sends; the copilot never sends on its own. We pull the relevant record, the right template and approved reference material into every request, so drafts start about 80% right instead of generic.
Best for: Teams writing similar documents many times a day
- Context pulled from your CRM or case system
- Tone and length controls users understand
- Edit tracking to measure real time saved
- 02
Document generation
Complete documents assembled from data and approved clauses: statements of work, policy documents, reports, tenders and client packs. Fixed wording stays fixed, and the model only writes the parts that genuinely vary. Output lands as Word, PDF or HTML in your house format, with every generated paragraph traceable to its inputs.
Best for: Legal, consulting, insurance and property teams with templated documents
- Clause libraries with locked and variable sections
- Output in your Word or PDF templates
- Version history and approval trail
- 03
Summarisation and analysis
Long inputs turned into short, decision ready outputs: meeting notes, call transcripts, due diligence packs, research papers, customer feedback or incident logs. We design summaries around the decision the reader has to make, with quotes and page references for every key point, so nobody has to trust a paraphrase blindly.
Best for: Managers, analysts and reviewers reading more than they can keep up with
- Summaries structured around a decision
- Quotes and references for each claim
- Theme analysis across hundreds of documents
- 04
Structured data extraction
Invoices, contracts, forms, emails and scanned letters turned into clean fields that drop straight into your systems. Models return data against a strict schema, and validation rules check dates, totals and identifiers before anything is written. Low confidence fields go to a person with the source highlighted. This is often the fastest payback in generative AI.
Best for: Finance, operations, logistics and claims teams keying data by hand
- Schema constrained JSON output
- Arithmetic and cross field validation
- Review queue for low confidence fields
- 05
Content at scale with brand controls
Product descriptions, listing copy, localised pages, ad variants and email campaigns produced in the thousands without drifting off brand. Every item passes through automatic checks for voice, banned claims, required disclosures and factual consistency with your product data, and a sample goes to an editor each batch. Nothing publishes without passing.
Best for: Ecommerce, real estate and marketing teams with large catalogues or many markets
- Brand voice rules and banned phrase lists
- Fact checks against your product data
- Batch review and publish workflow
- 06
Multimodal understanding
Applications that read images, scanned documents, diagrams, photos and audio as well as text. Examples include checking photos of completed work against a job sheet, reading equipment nameplates, interpreting charts in reports or turning recorded calls into structured notes. Current models from OpenAI, Anthropic and Google accept images and PDFs directly.
Best for: Field services, insurance, healthcare and inspection teams
- Photo and scan interpretation
- Speech to text with speaker labels
- Combined image and text reasoning
- 07
Internal code and data assistants
Assistants that help your own engineers and analysts: explaining a legacy codebase, writing tests, generating SQL from plain questions against your warehouse, or migrating scripts. We connect them to your repositories and schemas through Model Context Protocol servers, with read only access by default and review before any change is merged.
Best for: Engineering and data teams with large internal codebases
- Natural language to SQL with guardrails
- Codebase aware explanations
- Read only by default, reviewed writes
How we keep generative AI output accurate, on brand and safe
A model that is right 90% of the time is a liability if nobody knows which 10% is wrong. Quality in generative AI is designed in layers, and no single layer is enough.
Clients rarely lose trust in a generative AI system because of one spectacular failure. They lose it through a slow drip of small errors: a wrong figure, an outdated clause, a tone that is slightly off. Our generative AI development services put five controls around every application so those errors are caught before users see them.
- Grounding. The model writes from facts we give it, pulled from your records, product data or approved documents, rather than from memory. Where knowledge is large or changes often, we connect the app to a retrieval layer, described on our enterprise RAG and AI search page.
- Templates and schemas. Structure removes most failure modes. Fixed sections, locked legal wording, JSON schemas for extracted data and length limits mean the model only generates the parts that need judgement.
- Review workflows. Output is routed by risk. Low risk drafts go straight to the user to edit; outputs that reach customers, money or regulators need an approval step with the source material shown side by side.
- Evaluations. Each feature has a test set of 100 to 300 real inputs with expected outputs or scoring rules. It runs on every prompt, template or model change, with a mix of rule checks, model graded scoring and human spot checks.
- Brand and legal checks. Automatic checks flag banned claims, missing disclosures, competitor names, regulated phrases and off brand terms, using rules your marketing and legal teams can edit themselves.
The layers support each other. Templates make evaluation easier because outputs are predictable; evaluation shows where review is still needed; review feedback becomes new test cases. Over a few months the share of outputs needing edits falls, and you can see it fall on a dashboard instead of taking it on faith.
We also measure what matters to the business rather than model scores alone: edit distance between draft and final version, time from request to approved output, and the rate of outputs rejected by reviewers. If those do not improve, the feature is not working, however clever the model.
Generative AI for business, by industry
The same patterns show up everywhere, applied to different documents and different rules about what may be said.
Law firms
Drafting first versions of letters, clauses and research memos from matter files and precedent banks, with citations and a mandatory lawyer review before anything leaves the firm.
More matters per fee earner
Read moreHealthcare
Summaries of referrals and records, patient letter drafts and prior authorisation packs, built to HIPAA requirements and never making clinical decisions.
Less time on paperwork per patient
Read moreEcommerce and retail
Product descriptions, attribute extraction from supplier sheets and localised copy across markets, checked against catalogue data and brand rules before publishing.
Faster listings, cleaner catalogue data
Read moreReal estate
Listing descriptions, market summaries and lease abstracts, with fair housing and advertising checks built into every draft.
Listings live sooner, fewer compliance edits
Read moreHome services
Quotes and job summaries generated from technician notes and photos, plus follow up messages that match what was actually done on site.
Quotes sent the same day
Read moreFinancial services and insurance
Claims correspondence, policy document summaries and data extraction from statements and forms, with audit trails and approval steps.
Shorter handling time per case
Professional services and consulting
Proposal and statement of work generation from past engagements, research synthesis and client report drafting in the firm’s house style.
More proposals with the same team
B2B software companies
Generative features inside the product: drafting, summaries of customer records, natural language reports and in app help.
Feature adoption and retention
Choosing a model: OpenAI, Claude, Gemini or open weight?
Model choice is a measured decision on your own examples, not a brand preference. We usually shortlist two or three and let a test set decide.
Every major provider now ships a family rather than a single model: a large model for hard reasoning and long documents, and smaller, faster, cheaper variants for high volume tasks. The right answer is often a mix, with a small model handling classification and extraction and a larger one writing the sections that need judgement. Here is how we think about the main options:
- OpenAI GPT 5 family. Strong general reasoning, tool use and structured output, with the widest ecosystem. Available directly or through Azure OpenAI when you need Microsoft’s enterprise controls and regional hosting.
- Claude by Anthropic. Excellent at long documents, careful writing and following detailed instructions, which suits drafting, analysis and coding work. Available directly, on AWS Bedrock and on Google Vertex AI.
- Gemini by Google. Very long context windows and strong multimodal input across images, audio, video and PDFs, with competitive pricing on its faster tiers. Natural fit for Google Cloud and Workspace customers.
- Open weight models: Llama by Meta and Mistral. Can run on your own servers or private cloud, so data never leaves your environment. Usually behind the frontier on hard reasoning, but often good enough for extraction, classification and summarisation, and cheaper at high volume.
We compare candidates on four numbers: quality on your test set, latency, cost per completed task and data handling terms. A model that costs a tenth as much and scores 3% lower is often the right production choice for a high volume step.
Whatever we pick, every call goes through a model gateway in your code. Switching providers, or routing a task to a newer model, is a configuration change followed by a test run rather than a rewrite. That protects you from price changes, model retirements and the next release that shifts the rankings again. If data sovereignty is the deciding factor, our note on private and self hosted LLMs covers the hosting options.
Generative AI consulting, in house build, or a partner who does both?
There are three common routes to a working generative AI application. Each has a place, and the right one depends on your team and how quickly you need results.
| Generative AI consulting firm | In house team on model APIs | Vrisic consulting and development | |
|---|---|---|---|
| Strategy, use case ranking and governance | Yes | If you have the expertise | Yes |
| Working software at the end | Often a prototype or deck | Yes | Yes |
| Evaluation and quality controls | Recommended, rarely built | Depends on experience | Yes |
| Speed to a first release | Slow, strategy first | Varies with hiring | 4 to 8 weeks for a copilot |
| Builds lasting internal skills | Some | Yes | Through pairing and handover |
| Pricing model | Day rates or retainers | Salaries and tooling | Fixed price per phase |
| You own code, prompts and test sets | Varies | Yes | Yes |
| Best when | Board level strategy across many units | You have spare senior AI engineers | You want one team accountable from idea to production |
Large consultancies are the right choice for enterprise wide transformation programmes, and a strong in house team wins long term if you can hire it. We compare providers openly in our list of the top generative AI development companies, and explain how generative and agentic systems differ in agentic AI vs generative AI.
Generative AI development pricing
Typical ranges for custom generative AI development at three levels. You get one fixed price in writing after a free scoping call, including the discovery and consulting work.
Focused copilot
One high volume task for one team, such as drafting replies or extracting invoice data
$15,000 to $45,000£12,000 to £36,000AED 55,000 to AED 165,000
4 to 8 weeks
- Use case discovery and model shortlist
- Templates, prompts and one integration
- Evaluation set and quality report
- Simple web app or add in for existing tools
- Deployment in your cloud account
Product feature or internal app
Several workflows, user roles and integrations used daily by staff or customers
$45,000 to $120,000£36,000 to £96,000AED 165,000 to AED 440,000
8 to 16 weeks
- Multiple generation and extraction workflows
- Grounding in your data and documents
- Review and approval workflows
- Brand and legal rule checks
- Evaluation in CI and live monitoring
- Cost tracking per user or customer
Generative AI product
A new product or product line where generative AI is the core value
From $120,000From £96,000From AED 440,000
4 to 8 months
- Product design and multi tenant architecture
- Several models with routing and fallbacks
- Billing, usage metering and admin tools
- Red teaming and safety testing
- Launch support and roadmap planning
Typical ranges; you get one fixed price in writing after a free scoping call. Model and hosting usage is billed at cost, managed support starts at $1,500 per month, and annual running costs typically land at 15% to 30% of the build cost. For context, one established vendor quotes entry level generative AI work from about $5,000 and custom solutions typically at $30,000 to $40,000 (Itransition, accessed September 2026), and Clutch reports an average AI project of about $120,600 (Clutch, updated September 2026). More detail in our AI development cost guide.
What drives the cost of a generative AI build?
The model itself is rarely the expensive part. These are the factors that decide where a project lands in the ranges above.
| Cost driver | Effect on budget | Why it matters |
|---|---|---|
| Consequence of a bad output | High | Internal drafts a person always edits need light checks. Customer facing, legal or clinical output needs larger test sets, approval steps and audit trails. |
| Integrations and data access | High | Grounding needs data. Each CRM, document store or database the app reads from or writes to adds authentication, mapping and testing. |
| Number of distinct workflows | High | Each type of document or task needs its own template, test set and review rules. Three workflows cost far more than one done well. |
| Brand and legal rule depth | Medium | A short style guide is quick to encode. Regulated disclosures across several markets and languages take real design and legal review time. |
| Input messiness | Medium | Clean digital text is easy. Scans, handwriting, photos and mixed languages need OCR, multimodal models and more validation. |
| Hosting constraints | Medium | Managed APIs are fastest to build on. Self hosted open weight models or in country hosting add infrastructure and operations work. |
| Interface polish | Low | An add in for Outlook, Word or Slack is quick. A customer facing product with onboarding and billing needs more design and testing. |
Send us five real examples of the work you want AI to do
On a free 30 minute scoping call we look at your examples, tell you honestly whether generative AI fits, which model family we would test first and what a first release would cost.
How a generative AI development project runs
Consulting and engineering happen in one team and one plan, with a working prototype before the main build is priced.
- 01
Use case workshop
1 weekWe rank candidate use cases by value, feasibility and risk with the people who do the work today. Some ideas are better served by a rule, a form or an off the shelf tool, and we say so. The output is one or two use cases worth building first.
You get: Ranked use case list, Risk notes, Success measures
- 02
Examples, templates and test set
1 to 2 weeksWe collect 100 to 300 real examples of inputs and good outputs, write the style and legal rules down, and design templates. This is the generative AI consulting work that makes everything after it measurable.
You get: Evaluation set v1, Templates and style rules
- 03
Model bake off and prototype
1 to 2 weeksWe test two or three models against the evaluation set and report quality, speed and cost per task. A clickable prototype on real data lets users react before we build the full application.
You get: Model comparison report, Working prototype, Fixed build price
- 04
Build in two week sprints
3 to 10 weeksInterface, integrations, grounding, review workflows and rule checks are built in short cycles with demos every two weeks. The evaluation set runs on every change so quality never silently slips.
You get: Working releases, Automated evaluation in CI
- 05
Hardening and launch
1 to 2 weeksPrompt injection and misuse testing against the OWASP Top 10 for LLM Applications, load and cost testing, and a staged release to a first group of users with feedback captured inside the tool.
You get: Security test notes, Runbook, Launch dashboard
- 06
Measure and improve
OngoingWe track edit rates, approval rates, time saved and cost per task, review samples weekly and ship improvements. You can keep us on support, move to a retainer or take the system fully in house.
You get: Monthly quality and cost report
Technology behind our generative AI solutions
Mainstream, well documented tools, so your team or any future partner can maintain what we build.
Language and multimodal models
Chosen per task on your test set, with a cheaper model wherever quality allows.
Model hosting
Enterprise controls, regional hosting and private networking.
Orchestration and grounding
Multi step generation, tool calls and retrieval.
Documents and speech
OCR, layout parsing and transcription for messy inputs.
Application
Durable workflows for long generation and review jobs.
Evaluation and observability
Every prompt, output and score traced and comparable.
Security, IP and responsible generative AI
Generative AI raises questions ordinary software does not: who owns the output, what the model provider keeps, and what happens when someone tries to trick the model.
You own the work
All IP in the code, prompts, templates, evaluation sets and outputs we create is assigned to you on payment. There is no licence fee to keep using what you paid for.
Copyright and provider indemnities
OpenAI, Microsoft, Google and Anthropic each offer some copyright indemnity for outputs under their commercial terms, with conditions. We choose tiers and settings that keep you inside those conditions and note them in the handover.
No training on your data
We use business API tiers that do not train on your prompts or outputs under standard terms, document retention settings, and use private hosting when policy requires it.
PII redaction and logging
Personal data is masked before it reaches a model where the task allows, and prompts, outputs and approvals are logged under your retention rules.
Misuse and injection testing
Every application is tested against the OWASP Top 10 for LLM Applications, including prompt injection through uploaded documents and attempts to extract system instructions.
Transparency and compliance
We design for HIPAA, GDPR, UK GDPR, CCPA/CPRA and UAE PDPL, and for the EU AI Act Article 50 duties on disclosing and marking AI generated content, which apply from 2 August 2026. We hold no certifications we would need to claim.
Generative AI development in the US, UK and UAE
Copyright, privacy and language rules for generated content differ by market. This is how they shape what we build.
United States
The US Copyright Office concluded in January 2025 that output generated purely by AI is not copyrightable, while human selection, arrangement and editing can be protected (US Copyright Office, Part 2 report). We design workflows so a person makes meaningful creative choices where ownership matters, and follow HIPAA and state privacy laws.
- Human authorship built into content workflows
- BAA route for healthcare applications
- US hosting regions by default
- Pricing in USD
United Kingdom
UK law has protected computer generated works under section 9(3) of the Copyright, Designs and Patents Act 1988, but the government’s 2026 report on copyright and AI signalled that provision could be removed. Privacy runs under UK GDPR and the Data (Use and Access) Act 2025. Our UK buyer’s guide covers local questions.
- Contracts that assign rights whatever the law settles on
- AWS London or Azure UK South hosting
- British English templates and spelling
- Pricing in GBP
United Arab Emirates
UAE projects usually need content in Arabic and English, with Arabic that reads as native rather than translated. We test models on Arabic output with native reviewers, consider Arabic focused models such as Jais and Falcon, and host in country where the UAE PDPL, DIFC or ADGM rules call for it. See our Dubai and UAE AI guide.
- Arabic and English generation with right to left layouts
- Azure UAE North or AWS Middle East (UAE) region
- Native Arabic review in the test set
- Pricing in AED
How to choose a generative AI development company
Seven questions that separate teams who ship dependable generative AI from teams who ship impressive demos.
- 1
Will you show us a test set and quality report from a real project?
Generative output cannot be judged by trying a few prompts. A serious team will show how they score outputs, how they catch regressions and what their numbers looked like at launch.
- 2
How do you decide which model to use?
A good answer involves testing several models on your examples and comparing quality, speed and cost. A vendor tied to one provider, or unable to explain the tradeoffs, will cost you later.
- 3
What controls stop off brand or legally risky output?
Look for templates, rule based checks, review workflows and audit logs, not just a longer prompt. Ask to see how your legal team would update a banned claims list.
- 4
Who owns the prompts, templates and outputs?
You should, in writing. Prompts and test sets are the real intellectual property of a generative AI system, and some vendors quietly keep them.
- 5
Do your consultants also build and support the system?
Separating strategy from delivery creates a gap where accountability disappears. Ask who will be on the team after the contract is signed.
- 6
How will you control cost per task as usage grows?
Expect a cost per task estimate before launch, model routing to cheaper models where quality allows, caching and dashboards after launch.
- 7
What will you tell us not to build?
The best partners turn down weak use cases. If every idea is a good fit, the vendor is selling hours, not outcomes. You can also compare vendors in our guide to the best AI software development companies.
Questions we hear every week
Still unsure about something? Ask us on a call. We will give you a straight answer, even if it means we are not the right partner.
What are the top generative AI development companies?
There is no single ranking. Large consultancies such as Accenture, Deloitte and IBM Consulting suit enterprise transformation programmes. Engineering firms such as EPAM and Itransition offer big delivery teams. Smaller specialists, Vrisic among them, suit focused builds with senior engineers and fixed prices. Judge any company on evaluation practice, code ownership and relevant examples. Our guide to the top generative AI development companies compares them in detail.
What do generative AI development services include?
A full service covers use case discovery, model selection, prompt and template design, grounding in your data, application and interface development, integration with your systems, evaluation, security review, deployment in your cloud and support after launch. Some firms sell only consulting or only engineering. Ask for both in one team so the people who design the solution are accountable for making it work.
How much does generative AI application development cost?
A focused copilot for one task typically costs $15,000 to $45,000 over 4 to 8 weeks. A product feature or internal app with several workflows runs $45,000 to $120,000 over 8 to 16 weeks. A standalone generative AI product starts at $120,000. Model usage is billed at cost on top. Vrisic gives you one fixed price in writing after a free scoping call.
What is the difference between generative AI consulting and development?
Generative AI consulting decides what to build: which use cases pay off, which models and vendors to trust, what risks to manage and how to govern them. Development builds and runs the software. Consulting alone often ends with a slide deck and no working system. We fold consulting into a short paid discovery phase that ends in a prototype, a test set and a fixed build price.
Which LLM is best for business applications?
No single model wins everywhere. OpenAI’s GPT 5 family, Anthropic’s Claude and Google’s Gemini lead on general reasoning and writing, each with cheaper fast variants. Open weight Llama and Mistral models suit private hosting. We compare two or three candidates on your own test cases for quality, speed and cost per task, and build so you can switch later.
Who owns the copyright in AI generated content?
It depends on the country and on human input. The US Copyright Office says purely AI generated material is not protected, while human selection, arrangement and editing can be. UK law still has a computer generated works provision, now under review. In every case, our contract assigns you all rights we hold in the software, prompts and outputs we create.
Will OpenAI, Anthropic or Google train their models on our data?
Not under their standard business API terms. OpenAI, Anthropic, Google Cloud, AWS Bedrock and Azure OpenAI state that API and enterprise data is not used for training by default. Retention for abuse monitoring varies, and some providers offer zero data retention for eligible customers. We document the setting for each provider and use private hosting when policy requires it.
Can generative AI write in our brand voice?
Yes, reliably, when the voice is written down. We turn your style guide and 20 to 50 approved examples into templates, banned phrase lists and automatic checks that score every draft for tone, terminology and claims before a person sees it. Fine tuning is rarely needed for voice. Structure and review catch far more problems than a cleverer prompt.
Is generative AI safe to use in regulated industries such as healthcare, finance and law?
Yes, when the design matches the risk. Keep regulated data in approved hosting, ground outputs in verified sources, record every prompt and output, and require a qualified person to approve anything that reaches a patient, client or regulator. We build to HIPAA requirements, UK GDPR and UAE PDPL, and keep drafting separate from decisions.
How long does it take to build a generative AI application?
A focused copilot usually reaches users in 4 to 8 weeks, and a production feature or internal app in 8 to 16 weeks. The first week or two sets up the test set and templates; after that you see working software every two weeks. Delays usually come from data access, legal review of outputs or late agreement on what good output looks like.
Can you add generative AI features to our existing SaaS product?
Yes. We build the feature as a module in your codebase, behind your existing authentication and permissions, with a model gateway that tracks cost per customer. Typical first features are drafting assistants, summaries of records and natural language reporting. We ship behind feature flags so you can release to a few customers, measure usage and cost, then widen.
Find the process where AI will pay off first
Message us on WhatsApp for the fastest reply, or book a free thirty minute call. You will leave with the processes most worth automating, whether to build new or upgrade what you have, a realistic timeline and a clear idea of cost, whether or not you work with us.