AI Development Services
Custom AI built into your product - agents, computer vision, NLP and decisioning, integrated into the stack and compliance boundary you already have.
AI development is the applied work of putting an AI capability into a product: an agent that does a task, a model that reads a document, a decision that used to need a person. This page is about that build. If your need is really custom models and predictions, see ML development; if it is LLMs and generation, see generative AI.
Results from work we have shipped
An AI-driven solution that optimises medical sales representative routes and improves field efficiency, using deep learning over visit patterns to predict the best routes.
AI we build
Agents that carry out a task end to end - gather inputs, call your systems, apply rules and escalate to a person on the edge cases.
Document capture, classification and verification, and image checks for onboarding, claims and operations.
Extracting fields, intent and meaning from documents, messages and forms, so downstream steps run on structured data.
Underwriting and risk models with the audit trail, override paths and challenger setup a regulator expects. Every decision is reproducible after the fact.
Transaction and behavioural models, real-time scoring and the explainability your fraud and compliance teams need to act on an alert.
Document and selfie checks, liveness and identity matching, with automated scoring across vendors and fallback to manual review where required.
Wiring a model, an API or an agent into an existing app, with the monitoring and guardrails to run it in production.
How we deliver AI development
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We map the use case, the data you can use and the governance constraints, then write a phased scope. A proof of concept can land in weeks.
A named lead decides where the AI runs, what its boundary is, how it is logged and how it is explained. Governance is part of the architecture, not a finishing layer.
We build the pipeline, train or wire the model, set up override and challenger paths, and integrate it into your stack.
Deployed in your cloud, monitored on its own targets, with a runbook handed to your team so they can run it without us.
What shapes the work
Most AI projects do not fail on the model. They fail because the model never reaches the data or the workflow it was meant to change. We build AI that ships: scoped to a real decision, integrated into your stack, and handed to your team to run.
An assistant that cannot reach the right data is a chatbot; a fraud model that cannot reach the payment stream is a research paper. So a large part of AI development is integration - which is why this work sits next to our API integration services and, for the wider platform, financial software development.
In a regulated operation, a decision the model cannot explain is a decision you cannot ship. We pick model classes and logging patterns that produce explanations by default, set up challenger models where a regulator expects them, and design override paths so a person can intervene with an audit trail. For the deeper modelling side of this - training, validation and the MLOps around it - see ML development.
If the input is language - documents, queries, conversations - the build usually leans on a language model. We treat that as generative AI, which is where LLMs, RAG and fine-tuning live. Two common products built on it have their own pages: AI chatbots for conversation, and AI-driven search for retrieval. On this page we keep to applied AI: agents, vision, NLP and decisioning, and the integration that makes them work.
We prove value before you commit to a build. A proof of concept on your own anonymised data is fixed-price and time-boxed, with one question to answer: does the model clear the bar your use case needs. If it does, production follows as time and material or an outcome-based arrangement, with governance and monitoring built in from the first sprint. See the full approach on the Data Science &
What is the difference between AI development and ML development here?
AI development is building an AI capability into a product - agents, vision, NLP, decisioning and the integration around them. ML development is the modelling discipline: training custom models, predictive and recommender systems, and MLOps. Many projects use both.
Can you add AI to our existing product rather than build from scratch?
Yes. A large share of this work is integration - wiring a model, API or agent into an app you already run, with the monitoring and guardrails to operate it.
Do you build AI agents?
Yes - agents that gather inputs, call your systems, apply rules and escalate to a person on the edge cases, with an audit trail throughout.
How do you keep the AI inside our compliance boundary?
Self-hosted or private-endpoint models where capability allows, data kept on your infrastructure, and audit logging where the use case requires it. The boundary is decided at the design step.
How fast can you prove value?
A focused proof of concept can land in weeks. On a route-optimisation engagement, three weeks on six months of anonymised data showed 30% less travel time and 20% more visits per day before any full build.
Who owns the result?
You do - source, pipelines, model artefacts and documentation, running in your cloud from the start.
WislaCode helped Verysell Group Applied AI Lab explore the current state and future of AI testing, turning uncertainty into clear, actionable insights.
View case
A private, on-premise WislaSearch deployment that turns a logistics operator's scattered operational knowledge into instant, cited answers - without anything leaving their network.
View case
WislaSearch is an AI-powered search assistant that centralises fragmented data, indexes and retrieves the right information instantly, and keeps everything inside your own secure infrastructure.
View caseThis was a very task-heavy project, mostly exploration and R&D-driven. However, by the end of WislaCode, we were left with a detailed roadmap consisting of clear milestones - able to be converted into tangible KPIs - and some neat ideas of what actionable are next. Integrating...
We collaborated with WislaCode on a product strategy development project and gave the highest marks for this contractor. The WislaCode team delivered on time and with outstanding quality.
We collaborated with WislaCode on a route-to-market optimisation project. Working with WislaCode was effective, transparent and predictable, which is especially critical for AI and ML projects. We provided them with six months of anonymised data, and within just three weeks...
Have an AI feature in mind?
Bring the decision you want to automate or the capability you want to add. We will scope a proof of concept that answers whether it works, fast.