Data & AI
Generative AI Development
Put generative AI to work on the text, images and conversations your business produces every day, with founder-led delivery, grounded answers and a person checking what matters.
Talk to the foundersWhat generative AI is, and what you get when we build it
Generative AI uses models trained on huge amounts of text, images and audio to produce new content on request: a reply to a customer, a summary of a contract, a product description, a voice prompt or a design concept. On its own it's impressive and unreliable. Connected to your data, your rules and your review process, it becomes a dependable tool.
That connection is what we build. The founders help you choose the few use cases where generative AI clearly pays off, a senior engineer leads the build, and we ground every answer in your own sources, test it against real examples and keep a person in the loop wherever an error would be costly.
Your prompts, data pipelines and evaluation sets belong to you, and we design the system so you can change model provider later without starting over.
What we do
Our generative AI solutions
Four kinds of generative AI cover most of what businesses need. We often combine them in one product.
Conversational assistants
Chat and voice assistants for customers and staff that answer from your knowledge base, help with forms and orders, and pass the conversation to a person when they reach the edge of what they should handle. Every answer can cite its source, and conversations feed back into improving the assistant.
Writing and summarising tools
Tools that draft emails, reports, product listings and proposals in your tone of voice, summarise long documents and meetings, and pull key facts out of contracts and forms. Your team stays the editor: the AI does the first draft, people approve the final one.
Speech and audio
Transcription and summaries of calls and meetings, natural-sounding voice responses for phone lines and apps, and audio clean-up and translation, so spoken information becomes searchable and useful.
Image generation and editing
Image models that produce concept art, product shots on new backgrounds, variations for campaigns and visuals for personalised content, inside clear brand rules and with a designer approving what gets published.
Customer feedback
Working with Authect transformed our digital infrastructure. Their engineering team delivered our complex trading algorithm platform two weeks ahead of schedule without sacrificing code quality or security compliance. The seamless communication and deep technical expertise made them a true strategic partner.
Dmitry SokolovHead of Product, FinTech SolutionsReady to find out what generative AI could do for your business? Ask the founders.
Contact usService benefits
The value of generative AI for your business
The best results come from pairing generative AI with the people who know your work. These are the gains we design for.
- 01
Faster creative and design work
Generate many first drafts, layouts or concepts in the time it used to take to make one, so designers and engineers explore more options and spend their effort refining the best.
- 02
Content at scale, personalised
Product descriptions, localised copy and tailored messages produced from your own data and style guide, reviewed by your team, so you reach more people without hiring an army of writers.
- 03
More engaging customer experiences
Interfaces that respond to each customer in natural language, recommend what fits them and explain options clearly, making it easier to find, choose and buy.
- 04
Knowledge your team can actually find
Ask a question in plain words and get an answer drawn from policies, manuals and past tickets, with a link to the source, instead of searching through shared drives.
- 05
Less time on routine documents
Summaries, first-pass reviews and data extraction from contracts, claims and reports, so specialists check and decide rather than read and retype.
- 06
Control over quality and risk
Guardrails, evaluation and human approval steps designed in from the start, so you gain speed without publishing something you'd regret.
Industries
Industry-specific use cases for generative AI
Healthcare
Synthetic patient records for testing and training without exposing real data, summaries of clinical notes and research for staff to check, assistants that help patients prepare for appointments, and report drafting that saves clinicians time.
Retail
Product descriptions and campaign copy generated from your catalogue, personal shopping assistants that suggest the right product, simulated customer segments for testing offers, and assistants that help back-office teams with orders and suppliers.
Fintech
Reconciliation support that matches statements and records by meaning rather than exact text, assistants that explain products to customers, summaries of regulatory updates, and first drafts of compliance reports for your team to review.
Media & Entertainment
Drafting support for editorial teams, automatic tagging and summaries for archives, translation and subtitling, personalised newsletters and concept visuals for campaigns, with editors deciding what's published.
Insurance
Claims summaries from photos, forms and emails, policy assistants that answer customer questions in plain language, first drafts of correspondence, and underwriting support that pulls key facts from long documents.
Logistics
Assistants that answer shipment questions from live tracking data, extraction of details from invoices and customs documents, and drafted updates to customers when deliveries change.
How we work
How we deliver generative AI solutions
Generative AI projects move fast, but every stage ends with something you can test and judge before the next one starts.
- 01
Discovery
The founders review your use case, the data and documents involved and the risks, and agree with you how quality will be measured.
- 02
Solution design
We choose the models, retrieval approach and guardrails, and plan the architecture, data flows, costs and review steps.
- 03
Prototype
A working prototype on your own content, tested against an evaluation set of real examples, so you see its strengths and limits.
- 04
Build and deploy
The production version, integrated with your systems, with monitoring, access controls and the tools your team uses to review results.
- 05
Improve
We track quality, cost and feedback after launch and keep refining prompts, data and models as real use teaches us more.
Have a generative AI idea? We'll help you test it on your own data before you commit.
Contact usCase studies
Our generative AI work
FintechTradingArtificial IntelligenceSmartTraderSmartTrader: Technical Analysis & Automated Trading Platform100k+Active Traders< 20msLatency50+Smart Tools
FintechArtificial IntelligenceConsumer AppMeet CleoCleo: The AI Financial Assistant Built to Budget, Save & Roast Your Wallet5.9B+Messages1.1M+Subscribers2.95%Savings APY
Responsible AI
How we keep generative AI accurate, safe and yours
- 01
Grounded in your sources
Answers are built from your approved documents and data, with citations, so people can check where every statement came from.
- 02
Tested before every release
An evaluation set of real examples runs whenever prompts, data or models change, so quality doesn't quietly slip.
- 03
People approve what matters
Anything that reaches a customer or drives a decision can go through a review step, and every output is logged for traceability.
- 04
Private by design
We use provider offerings that don't train on your data, anonymise sensitive details where needed and keep data in the regions you require.
- 05
No lock-in
Prompts, pipelines and evaluation sets live in your accounts, and the system is designed so you can switch model providers later.
- 06
Honest advice
If a template, a rule or a better search would solve the problem more reliably than a model, we'll say so.
Insights
NewsWeekly briefing, 4 November 2026: the $6.3bn Binance heist, HTTP2Shell keeps spreading, and one robots.txt line for AI trainingView news 
R&DHow to evaluate LLM features in production: test sets, judges, metrics and budgetsView R&D post 
GuideWhat are AI development services? A practical buyer's guideView guide 
GuideHow to add AI to your product: a practical guide to LLM featuresView guide 
ArticleThe 12 best AI development companies in 2026View article 
R&DRAG in production: what actually decides answer quality?View R&D post
FAQs
Generative AI FAQs
What is generative AI?
Generative AI is AI that produces new content on request, such as text, images, audio or conversation. It uses models trained on huge amounts of data to draft a customer reply, summarise a contract, write a product description or create a design concept. On its own it's impressive but unreliable; connected to your data, your rules and your review process, it becomes a dependable business tool.
How is generative AI different from traditional machine learning?
Traditional machine learning predicts or classifies, while generative AI creates. A machine learning model might score a transaction for fraud or forecast demand; a generative model writes, summarises, answers questions or produces images. Many products combine both. And if a template, a rule or a better search would solve your problem more reliably than any model, we'll tell you.
How do you stop generative AI from making things up?
We ground answers in your own approved sources, with citations, so people can check where every statement came from. An evaluation set of real examples runs whenever prompts, data or models change, and anything that reaches a customer or drives a decision can go through a human review step. Every output is logged. No system is perfect, so we design for mistakes to be caught early.
How long does a generative AI project take?
It depends on the use case and the integrations, but you see results early. Every stage ends with something you can test: after discovery and solution design comes a working prototype on your own content, tested against real examples, so you can judge its strengths and limits before committing to the production build. After discovery you get a plan for the stages ahead.
How much does generative AI development cost?
It depends on scope: the use case, the data and documents involved, the integrations and review steps, plus model usage once it's running. We quote each project individually after a free scoping conversation with the founders, and plan running costs as part of solution design so there are no surprises later. For a rough first idea of the development side, try the software development cost calculator on our website.
Is our data safe, and can we switch model providers later?
Yes to both. We use provider offerings that don't train on your data, anonymise sensitive details where needed and keep data in the regions you require. Your prompts, data pipelines and evaluation sets live in your accounts, and we design the system so you can change model provider later without starting over.