Add AI Capabilities to Your Product
We help product teams add useful AI to existing software from computer vision and recommendations to forecasting, document intelligence, LLM features and conversational AI. We choose the right approach, integrate it into the product, and test it against the user experience and business outcome.
Where AI can improve your product
Start with a user problem or product metric, not a model. AI should make a feature more useful, faster, easier to use or possible for the first time.
01
See and understand visual information
Recognise, classify and analyse images or video to support inspection, search, quality control, safety and other visual workflows.
02
Personalise the experience
Recommend products, content or next-best actions using behaviour, context and business rules — while giving product teams control over what is optimised.
Add the right AI feature — not AI everywhere
We identify where AI changes the product experience, define how success will be measured, and choose the smallest reliable approach before integrating it into your architecture.
03
Predict what happens next
Forecast demand, workload, risk or other future outcomes when a prediction can improve the decision your user needs to make.
04
Work with language and knowledge
Let users search, summarise, extract, generate or ask questions over product and business knowledge with clear grounding and permissions.
What makes an AI feature production-ready
A model demo is not a product feature. The feature needs measurable quality, predictable latency and cost, safe fallback behaviour and a way to improve after release.
A useful AI feature starts with a user job and a measurable outcome. The model is a design choice, not the reason the feature exists.
Product problem first
User task → success metric → AI approach
A useful AI feature starts with a user job and a measurable outcome. The model is a design choice, not the reason the feature exists.
Product problem first
User task → success metric → AI approach
12%
Increase in NPS
Average NPS Growth Achieved by Our Clients Who Implemented AI in Their SaaS Products
Latency, availability and fallback behaviour are product requirements. If AI is unavailable or uncertain, the user still needs a safe path forward.
Latency, availability and fallback behaviour are product requirements. If AI is unavailable or uncertain, the user still needs a safe path forward.
Privacy, permissions and data boundaries are designed around the product workflow and deployment model — especially when user or company data is sent to an external model.
12M USD
Cumulative Investment
Received by Our Clients in New Funding Rounds After Implementing AI as Part of Their Solutions
Privacy, permissions and data boundaries are designed around the product workflow and deployment model — especially when user or company data is sent to an external model.
AI capabilities we can add to your product
We can add one focused capability or combine several. Each feature is designed around your users, product architecture, data, security requirements and operating constraints.
AI Product Strategy & Technical Design
Define the user problem, acceptance criteria, data and model options, architecture, privacy boundaries, latency and cost targets, and a staged rollout plan.
Computer Vision
Add image and video understanding for classification, detection, search, inspection, quality control or other product workflows with measurable visual accuracy.
Recommendations & Personalisation
Recommend products, content or next-best actions using behaviour, context and business rules, with product metrics that show whether recommendations are actually useful.
NLP & Document Intelligence
Extract, classify, search and summarise information from documents, messages and other text while preserving source references and access boundaries where needed.
Time-Series Forecasting
Forecast demand, revenue, workload, capacity or other time-dependent outcomes and expose the prediction inside the workflow where users make the decision.
LLM Integration & Engineering
Select and integrate language models around the feature’s quality, context, latency, privacy and cost requirements. Keep provider choice separate from product logic so models can be evaluated and changed safely.
Generative AI Features
Add drafting, rewriting, summarisation, transformation or media-generation features with product-specific controls, evaluation and clear user review where needed.
Conversational AI & RAG
Build assistants and chat experiences that retrieve relevant product or company knowledge, respect permissions, expose sources where useful, and fail safely when evidence is missing.
Our success stories
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Roman Reznikov
CEO & Partner
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