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Data & AI

AI Development Services

We design, build and run AI features and products that solve a real business problem, with the founders and a senior engineer leading every project and people reviewing what the AI does.

Talk to the founders

What you get with AI development from Authect

AI is only worth building if it makes a measurable difference to your users or your team. We start there: the founders work with you to find the problem worth solving, check that your data can support it, and agree what good looks like before a single model is chosen.

Then we build it end to end, from the first prototype to a feature running in production. Every project is led by a senior engineer, every AI output that matters is evaluated against real examples, and your data stays private: it is never used to train someone else's model without your agreement.

You own the result. Prompts, pipelines, models, evaluation sets and infrastructure live in accounts you control from day one, so there is no lock-in to us or to any single AI provider.

Our solutions

AI solutions and technologies we deliver

We pick the simplest kind of AI that does the job well, and we're honest when a rule or a good search box beats a model.

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Generative AI solutions

Assistants, drafting tools and content generation built on large language and image models, grounded in your own documents so answers are accurate, cited and on brand.

Machine learning applications

Models trained on your data to forecast demand, score risk, recommend products or flag anomalies, with monitoring that tells you when a model starts to drift.

Computer vision

Systems that read images and video: inspecting products for defects, counting and tracking objects, reading documents and checking photos submitted by customers.

Conversational AI

Chat and voice assistants for customers and staff that understand natural language, act on your systems and hand over to a person when they should.

AI agents

Agents that plan and carry out multi-step tasks across your tools, with clear permissions, logs of every action and a human approval step where the stakes are high.

Document understanding

Extracting, classifying and summarising information from contracts, invoices, forms and reports, so your team checks results instead of typing them in.

Search and retrieval

Semantic search across your knowledge base, tickets and files, so people find the right answer by asking a question in their own words.

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 Solutions

Have an AI idea you'd like to test against real data? Talk to the founders who would build it.

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Service benefits

How AI development can change the way your business works

  1. 01

    More done with the team you have

    AI takes on the repetitive reading, sorting, drafting and checking that fills your team's day, so people spend their time on the decisions and conversations that need them.

  2. 02

    Faster, more personal customer experiences

    Assistants that answer at any hour, recommendations that reflect what each customer actually wants, and replies drafted in seconds for your team to approve and send.

  3. 03

    Better decisions from the data you already hold

    Forecasts, risk scores and summaries that turn scattered records into a clear next step, with the reasoning shown so people can judge whether to trust it.

  4. 04

    Lower operating costs, carefully

    We aim AI at the processes where errors and manual effort are most expensive, measure the result against how things worked before, and stop if it doesn't pay off.

  5. 05

    Stronger security and fraud detection

    Models that spot unusual logins, payments or behaviour early and route them to the right person, alongside the security practices we build into every project.

  6. 06

    An edge your competitors can't copy overnight

    AI built around your own data, workflows and customers is hard to replicate with an off-the-shelf tool, and it improves as your business grows.

Industries

Industry-specific use cases for artificial intelligence

Healthcare

Triage and booking assistants, summaries of patient notes for clinicians to check, support for reading medical images, remote monitoring alerts and personalised care plans, all designed with patient privacy and clinical oversight from the first line of code.

Doctor and patient reviewing a tablet at a desk with a stethoscope
Retail

Product recommendations and visual search, demand forecasting and stock planning, dynamic pricing, shopping assistants, and computer vision for shelf checks and quality control.

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Media & Entertainment

Personalised content recommendations, tagging and metadata for large archives, drafting support for editorial teams, audience insights, and moderation that flags content for people to review.

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Fintech

Fraud detection and transaction monitoring, credit and risk scoring with explainable reasons, assistants for customer questions, and automated checks that support compliance and regulatory reporting.

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Insurance

Underwriting support, claims triage and document extraction, fraud signals on suspicious claims, policy assistants for customers and predictive models that help prevent losses.

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Logistics

Demand and capacity forecasting, route and load planning, delivery time predictions, document processing for shipments and customs, and assistants that answer where-is-my-order questions.

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Automotive

Predictive maintenance from vehicle and sensor data, visual inspection on production lines, service booking assistants and analytics on how fleets and connected vehicles are used.

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The founders' view: AI earns its place by being checked

The hard part of AI in production isn't getting a model to answer. It's knowing when the answer is wrong, and making sure that never reaches a customer unnoticed.

So we treat every AI feature like any other critical system: we define the inputs it should accept, validate what it returns, test it against real examples before every release and keep people in the loop where a mistake would be costly.

What that means on your project:

  • An evaluation set built from your real cases, run before every release
  • Validation of model inputs and outputs, not just a well-written prompt
  • Human review wherever an error would affect a customer or a decision
  • Clear logs of what the AI did and why, so issues can be traced and fixed
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How we work

How our AI development services are delivered

Each phase ends with something you can see and judge: a written assessment, a working prototype, then a feature in production. You can stop or change direction at any point.

  1. 01

    Discovery

    Workshops with the founders to understand the problem, the people affected and the data available. We look at real examples together and agree how success will be measured.

  2. 02

    Solution design

    We choose the approach, from a prompt and retrieval over your documents to a custom-trained model, and outline the architecture, data flows, privacy controls, costs and a plan you can review.

  3. 03

    Prototype and evaluation

    A working prototype on your own data, measured against the evaluation set. You see where it performs well and where it doesn't, before committing to the full build.

  4. 04

    Development and integration

    We build the production version into your product or workflows, with guardrails, monitoring, access controls and the interfaces your team needs to review and correct results.

  5. 05

    Launch and improvement

    A careful rollout, often to a small group first, then ongoing monitoring of quality, cost and drift, with regular improvements as we learn from real use.

Ready to see what AI could do with your own data? Start with a conversation.

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Why Authect

What makes Authect's AI development different

  1. 01

    You talk to the people who build it

    The founders scope your AI project and a senior engineer leads it. There's no salesperson between you and the team, so trade-offs are explained by the people making them.

  2. 02

    End-to-end, from idea to production

    We handle discovery, data preparation, model work, the application around it, infrastructure and support, so you don't have to coordinate several vendors.

  3. 03

    Software engineering first

    AI is one part of a working product. We bring the same code review, testing, security and documentation to AI features as to everything else we build.

  4. 04

    Privacy, security and honesty

    Your data stays under your control, sensitive information can be anonymised before it reaches a model, and we tell you plainly when AI isn't the right answer.

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Insights

Case studies

Our AI development work

Models we use

Models and platforms we build with

We aren't tied to any one AI provider. We choose the model that fits your task, budget and privacy needs, and design your system so you can switch models later without rebuilding it.

Where data mustn't leave your environment, we run open-source models on infrastructure you control.

Language and multimodal models

  • OpenAI models

    GPT models for reasoning, drafting, extraction and tool use, accessed through the API or enterprise cloud offerings.

  • Anthropic models

    Claude models for long documents, careful reasoning, coding tasks and agents that use tools.

  • Google models

    Gemini models for multimodal work across text, images, audio and video.

  • Open-source models

    Llama, Mistral, Qwen and similar models, run privately and fine-tuned on your data when that's the better fit.

Machine learning and infrastructure

  • Machine learning frameworks

    PyTorch, scikit-learn and gradient boosting libraries for forecasting, classification and computer vision models.

  • Vector search

    PostgreSQL with pgvector and dedicated vector databases for retrieval over your documents and data.

  • Cloud AI platforms

    AWS, Microsoft Azure and Google Cloud, so models run in the region and account your compliance needs require.

How we cooperate

Ways to work with us on AI

Many AI projects start with a fixed-price prototype to prove the idea, then move to monthly sprints as the feature grows. Responsibilities on both sides are agreed before work starts.

  • 01

    Fixed-price project

    A clearly scoped product delivered for an agreed price and timeline. Ideal for MVPs and well-defined projects.

    Our responsibility:
    the scope, plan, budget, quality and delivery of the agreed product.
    Your responsibility:
    business context, timely feedback and sign-off on the scope.
  • 02

    Monthly sprints

    A dedicated team, led by a senior engineer, working in short sprints, billed monthly. Ideal when your priorities evolve as you learn from users.

    Our responsibility:
    the team, sprint delivery, engineering quality and transparent reporting.
    Your responsibility:
    the product vision and setting priorities with us each sprint.
  • 03

    Support and evolution

    After launch, we keep your product fast, secure and up to date, and keep shipping improvements as your business grows.

    Our responsibility:
    monitoring, updates, fixes and agreed response times.
    Your responsibility:
    reporting issues and planning the next improvements with us.

FAQs

FAQs about AI development

How do we know whether AI is right for our problem?

We find out together in discovery. We look at real examples of the task, the data you have and what a mistake would cost. If a simpler approach would work better, such as rules, better search or a cleaner process, we'll tell you.

Is our data safe if we use models from external providers?

We use business offerings that don't train on your data, keep data in the regions you require, and can anonymise sensitive information before it reaches a model. Where data mustn't leave your environment, we run open-source models on infrastructure you control.

How do you stop AI from making things up?

We ground answers in your own sources and show where they came from, validate inputs and outputs, test against an evaluation set before each release, and keep a person in the loop wherever an error would matter. No system is perfect, so we design for mistakes to be caught early.

How long does an AI project take?

A focused prototype on your data can often be ready within weeks, which tells you quickly whether the idea works. A production feature takes longer, depending on integrations and review needs. After discovery you get a written plan with milestones.

Do we own the models and code you build?

Yes. Code, prompts, evaluation sets, fine-tuned models and infrastructure are yours from day one, in accounts you control. You can continue with us, take the work in-house or move it elsewhere.

Can you add AI to our existing product?

Yes, that's often the best place to start. We review your product and data, pick one feature where AI clearly helps, and build it in alongside your existing code with the same tests and review as everything else.

Tell us what you’re building. A founder replies within one working day, and builds it with you.

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