AI & Automation · Custom AI Solutions
AI built around your data, your process and your rules.
What is Custom AI?
Custom AI solutions apply AI models to problems specific to your business — extracting data from documents, searching internal knowledge, classifying requests, generating drafts or adding AI features to your own product.
Who is it for?
- Companies with large volumes of documents or requests
- Software products adding AI features
- Organisations with valuable internal knowledge
What problems does it solve?
- Manual processing of documents and forms
- Knowledge locked in files and people
- Off-the-shelf AI tools don't fit the workflow
How does EVERSIL approach it?
- 01
Feasibility
Test the idea with real data before committing to a build.
- 02
Design
Model choice, data flow, privacy and cost per use.
- 03
Build and evaluate
Integrated solution with measurable quality tests.
- 04
Operate
Monitoring of quality, cost and drift over time.
What does implementation include?
- Feasibility study
- Production AI feature or service
- Evaluation framework
- Security and data-flow documentation
How long does a typical project take?
Feasibility in 2–3 weeks; production builds typically 6–12 weeks.
What information do we need from you?
- Representative data samples
- Success criteria
- Security and compliance requirements
What should you consider before starting?
- Quality depends on data — budget time for preparation
- Ongoing model costs should be estimated up front
Especially relevant for
Questions businesses ask
Rarely necessary. Most business problems are solved by combining existing models with your data and rules. We recommend custom training only when evaluation shows it's needed.
Problems specific to your business that off-the-shelf tools don't solve well: searching and summarising internal documents, classifying requests, extracting data from files, drafting content from your own information or adding AI features to your product.
We run a short feasibility phase with real examples, test approaches and measure quality, cost and speed before recommending a full build.
We choose providers and settings based on their data-use terms and explain them before sensitive data is processed. Where required, we use options that keep your data out of model training.
Yes, through APIs and integrations — for example reading from your document store or CRM and writing results back.
We keep evaluation tests, monitor usage and errors and review outputs regularly, so quality issues are caught and prompts or models can be updated safely.
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