AI projects delivered from prototype to working solutions

Challenge

People who want to work with AI deliberately often stall at the start, because they do not know which tools to pick, in what order to adopt them and how to fit them to their own tasks and needs.

Solution

We run individual onboarding paths in which each participant builds their own AI Harness (a working AI assistant). Together we set up the tools, the memory of the role and the company as well as a library of prompts and skills. We work on real data, processes and tasks.

Challenge

Organisations considering AI often stall at the decision stage, because they do not know whether the idea will work on their data, what it will cost and how to measure its effect before committing a real budget.

Solution

We build rapid POCs and prototypes in which an AI idea is tested on the company's real data and processes. Together we agree the scope, the test cases and the success criteria and measures. The results form the basis for a recommendation and a directional decision.

Challenge

People in sales often struggle with one problem: researching companies and writing personalised messages consumes most of the time meant for selling.

Solution

We built a path from research to a finished message in which an AI assistant gathers signals about the company, proposes several drafts in an agreed tone and waits for approval before anything is sent (human-in-the-loop). Together we agreed the data sources, the tone of voice and the personalisation rules. We worked on a real contact list.

Challenge

Owners of smaller businesses often make pricing decisions on stale data, because they review competitor sites by hand, irregularly and only for part of the offer.

Solution

We build competitor offer and price scanners in which data from selected sites is collected daily, brought into one format and compared with the client's own offer. Together with clients we agree the list of competitors, the products to track and the change thresholds that trigger notifications.

Challenge

Back-office teams often spend a large part of the week on work that follows repeatable patterns. Administrative tasks consume the time of specialists whose skills and energy would be better spent on handling exceptions and making decisions.

Solution

We create process agents in which AI performs the repeatable steps and a human makes the decision at agreed approval points (human-in-the-loop). Together with process owners we write into the project charter the agents' scope of autonomy, their limits and the success criteria and measures.

Challenge

Employees of large organisations wait a long time for simple answers, because information from HR, finance and operations systems is scattered, delayed and often available only through a ticket to a specialist team.

Solution

We support teams and coordinate the build of production AI chatbots in which communication platforms are connected to the company's core systems. Together we agree the scope of questions, user permissions and the criteria and measures of answer accuracy.

Challenge

Entering a new market required a recruitment product in which an AI core optimally matches candidates to job offers.

Solution

We design and build AI-based HR platforms in which candidate matching is the core of the product. Together with the client's team we move from concept and requirements, through architecture and the matching model, to a working product.

Challenge

Individual investors often cannot see their whole portfolio in one place, because position and performance data sits across several platforms, each in a different format and with a different delay.

Solution

We build financial trackers in which data from many platforms is aggregated and consolidated in one view, making it easy to monitor positions, results, risks and market signals.

Challenge

Organisations with a distributed network of service and sales points often cannot keep up with answers. Queries arrive at every hour, scaling a helpline costs money, and most answers already live in the company's knowledge base.

Solution

We design production chatbots and voicebots in which answers come from the company's knowledge base (RAG) and a conversation without a confident answer goes to a consultant. Together we agree the scope of questions, the hand-over rules and the criteria and measures of answer accuracy. Before launch we test the bot on transcripts of real service calls.

Challenge

Smaller businesses are often hard to find: the offer is scattered across social media posts and printed materials, and search engines and AI assistants have nothing to index.

Solution

We build websites with sales materials in which the whole offer, prices and contact details sit under one address. Together we structure the services and prepare structured data to improve ranking in search engines and large language models (LLMs).

Challenge

In large corporations, searching through contracts, annexes and other documents consumes the attention and valuable time of procurement teams. Knowledge scattered across files makes comparing supplier contracts or finding specific clauses and deadlines slow and difficult.

Solution

We design AI solutions for procurement teams in which contracts are automatically indexed and read, and key fields, clauses and obligations are extracted into a searchable register. Together with the team we map the systems, contract fields, clause priorities and the way results are verified by a human.

Challenge

HR, Legal and Compliance teams in large organisations often cannot keep up with verifying and processing incoming documents. Many require a manual, time-consuming review for accuracy and completeness of information, consistent formatting and compliance with corporate and regulatory requirements.

Solution

We build automated AI document validation. Generative AI (GenAI) combined with optical character recognition (OCR) makes it possible to verify documents efficiently against agreed checklists and metrics. As a result, instead of spending time on repetitive tasks, the specialist focuses only on cases that genuinely require human intervention.

Challenge

Much of the work of a project management office is repetitive: constantly monitoring the status of initiatives, their budgets and resources, as well as risks and decisions. With many projects running in parallel and time and money pressure from the Board, mistakes are easy to make.

Solution

The answer is a GenAI assistant that answers questions about the project portfolio based on meeting transcripts, communication on MS Teams, Slack and WhatsApp as well as documentation kept in scattered repositories. Together with the PMO we agree the scope of documentation, the permissions required and the success criteria and measures.