What does this service deliver?
We build AI systems whose quality can be measured and monitored in production.
Success measures defined in advance
Clear data and access boundaries
Quality and cost monitored in production
Enterprise AI & RAG Solutions
Taking enterprise AI and RAG solutions into production.
We address the use case, data preparation, model choice, quality measurement and integration with existing systems as one production-focused project.
What is included?
We define the use case and cost of error before selecting a model.
We do not begin an AI project by choosing a model. We first establish which decision or task should improve, whether the available data is suitable, what errors would cost and where human oversight is required.
Depending on the need, we prepare data and develop search, assistant, forecasting, document or image-processing services together with an evaluation process. Before production, we test quality, latency, cost, security and failure scenarios against the agreed use case.
Service scope
What the engagement can include
Data preparation
We combine sources, assess data quality and build a dependable data pipeline for the selected model or service.
Forecasting and machine learning
We develop models for demand, risk, anomaly and behaviour forecasting around a defined business objective.
Generative AI and language processing
We develop assistants, search, summarisation and content-processing applications grounded in authorised business information.
Computer vision
We integrate OCR, object recognition and quality-control use cases into production workflows.
Model versioning and monitoring
We record and monitor model versions, quality changes, cost and service health.
Access and human oversight
We incorporate data access, output logging, confidence thresholds and human review where the use case requires them.
Technologies considered for the project
PythonPyTorchTensorFlowOpenAI APILangChainMLOps
The final stack depends on your current environment, team and requirements.Decision guide
When is this service a good fit?
When you need to turn an AI idea into a system that can operate in production.Current situation
There are many ideas but no clear priority
How we approach it
We rank use cases by business value, data readiness, cost of error and implementation effort.
Intended outcomeA prioritised set of use cases with documented rationale
Current situation
The pilot cannot move into production
How we approach it
We identify gaps in data flows, testing, security, integration and model monitoring.
Intended outcomeA service with defined test measures and operating owners
Current situation
Accuracy and data privacy remain concerns
How we approach it
We add access boundaries, test sets, output records and human review to the solution architecture.
Intended outcomeRestricted access and human review for higher-impact actions
Let us assess the use case, available data and cost of error together.Discuss your AI requirement ↗
Frequently asked questions
Common questions about data, quality and production use.
Will our data leave our environment for model training?+
Data flows, model providers, hosting and retention options are agreed at the start of the project. Sensitive-data boundaries are governed through architecture and contractual controls.
What is the difference between a proof of concept and a production system?+
Production requires dependable data pipelines, security, evaluation, cost and latency management, monitoring, error handling and clear ownership. We treat these as separate delivery requirements after a proof of concept.
How do you measure whether a model works well enough?+
We create a use-case-specific test set and quality measures, define error classes, establish human evaluation and monitor production behaviour.
Would you use an existing model or build a custom one?+
We compare existing models, adaptation, RAG and custom-model options against quality, data, latency, security and cost objectives.
How are timescale and cost determined?+
Timescale and cost are set after we understand the scope, current systems, integrations, data migration, testing and support requirements. We do not promise a fixed duration or price without this information.