AI that works onyour real data
We build LLM features, retrieval search and machine learning models into the products and workflows you already run, and we measure them against your own examples before anything ships.
Machine learning that earns its place in your product
Most AI projects stall in the gap between a promising demo and a feature people rely on every day. Our AI and machine learning development work is about closing that gap. We start with a specific job, such as answering support questions from your documentation, classifying incoming documents or forecasting demand, and decide whether a hosted LLM, a fine-tuned open model or a classic ML model is the right tool for it.
Before writing production code we build a small evaluation set from your real data: questions with known answers, labelled records, past outcomes. Every prompt change, model swap or retraining run is scored against it. That gives you a clear picture of how accurate the feature is, where it fails, and what it costs per request, so the decision to launch is based on evidence rather than a good-looking demo.
We also build the plumbing that machine learning depends on: ingestion pipelines, vector databases, feature stores, model serving and monitoring. If your data is spread across a CRM, a warehouse and a folder of PDFs, getting it clean and queryable is usually the first deliverable, and it stays useful whichever model you end up using. The same clean data also feeds your reporting and analytics, so the investment pays off beyond the first AI feature.
What our AI & Machine Learning service covers
- 01
LLM features in existing products
Summaries, drafting assistants, structured extraction and natural-language search added to your web or mobile app, with prompt versioning, output validation and cost limits per user.
- 02
Retrieval-augmented generation (RAG)
Search and Q&A over your documents, tickets and knowledge base. We handle chunking, embeddings, hybrid search, permissions and source citations so answers can be checked.
- 03
Custom machine learning models
Classification, forecasting, recommendation and anomaly detection models trained on your historical data, with a baseline to beat and a clear report on accuracy and failure cases.
- 04
Computer vision and document processing
Reading invoices, forms and IDs, detecting defects in images, or tagging media libraries, using OCR and vision models with a human review step where errors are costly.
- 05
Data pipelines and warehousing
Ingestion from your databases, SaaS tools and files into a clean, documented store that models, dashboards and analysts can all use without one-off exports.
- 06
MLOps and model monitoring
Experiment tracking, model registries, automated retraining and production monitoring for drift, latency and cost, so a model stays accurate after its first month.
What you get from working with us
Decisions based on measured results
An evaluation set built from your data shows how a feature performs before launch, and catches regressions whenever a prompt or model changes.
No lock-in to one model provider
We keep the model behind a thin interface, so you can move between OpenAI, Anthropic, Google or a self-hosted open model as prices and quality change.
Costs you can predict
Caching, smaller models for simple steps and per-feature usage tracking keep inference spend visible and under control as usage grows.
Your data stays governed
Access controls, PII handling and data retention settings are designed in from the start, and we document exactly what is sent to any third-party API.
Who this is built for
- SaaS products adding AI features that customers will pay for
- Teams with large document libraries that are hard to search
- Operations with historical data they want to forecast or classify from
- Companies whose AI prototype works in a demo but not in production
- Businesses that need AI but cannot send sensitive data to public APIs
From brief to launch, step by step
- 01Step 01
Use case and data audit
We pin down the task, the users and what a wrong answer costs, then review the data you have: where it lives, how clean it is and whether it is enough to work with.
- 02Step 02
Evaluation set and prototype
We assemble test cases from real examples and compare approaches against them, such as prompting, RAG, fine-tuning or a classic model, to find the simplest one that meets the bar.
- 03Step 03
Production build
The chosen approach becomes a proper service: APIs, pipelines, guardrails, fallbacks when the model is unsure, and a UI that shows sources or confidence where it helps.
- 04Step 04
Launch, monitor and retrain
We release to a limited group first, log inputs and outcomes, watch quality and cost, and set up a routine for re-scoring and retraining as your data changes.
Tools we trust
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- OpenAI API
- Anthropic Claude API
- LangChain
- Pinecone
- pgvector
- MLflow
- FastAPI
Questions buyers ask us
Straight answers about AI & Machine Learning. Anything else? Ask us directly.
It depends mostly on the state of your data, whether a hosted model is enough or a custom model has to be trained, how many systems we integrate with, and the accuracy the use case needs. Running costs for model APIs or GPUs are separate from build cost. We usually suggest a short discovery phase first; book a call and we will scope it with you.
A focused LLM feature on top of clean data moves faster than a custom model that needs labelled training data. The biggest variables are data preparation, how strict the accuracy requirement is, and how much review your compliance team needs. After a discovery call we give you a phased plan, starting with a prototype you can test against real examples.
Start with the simplest thing that passes your evaluation set. Hosted LLMs are often enough for language tasks, while forecasting, scoring and tabular prediction usually suit classic ML. Training or fine-tuning your own model makes sense when data cannot leave your infrastructure, request volume makes API costs too high, or the task is narrow enough that a smaller model does it better.
You do. The code, prompts, evaluation sets, pipelines and any model weights we train for you are delivered into your repositories and cloud accounts. Your data is used only for your project. If we build on a third-party model, its licence terms still apply to that base model, and we explain those terms before you commit.
We use API tiers that do not train on your data by default, strip or mask personal information where it is not needed, and keep a record of what is sent where. For data that must not leave your environment, we can deploy open models on your own cloud or servers. We go through these options with your security team before building.
Models drift as your data and users change, and provider models get updated. We set up monitoring for quality, latency and cost, keep the evaluation set running against new versions, and schedule retraining where it is needed. You can run this in-house with our documentation, or keep us on through a maintenance and support arrangement.
Ready to scope your AI & Machine Learning project?
A short call to understand your goals, constraints and timeline. You leave with a clear next step, with no obligation.