Searching company knowledge
Retrieval-augmented generation (RAG) to consult documents and procedures with source references. We define content updates, permissions and how to handle questions the data cannot answer.
ErinTech Labs / Applied AI
Applied AI for everyday work.
We design document search, classification and internal assistants connected to your tools. We start with a defined task, available data and a practical way to evaluate the result.
Tell us about your projectFrom an interesting prototype to a dependable process.
A convincing answer is not enough to use AI in a business. You need sources available to the right people, representative examples and clear behaviour when a model is wrong or lacks information. We address these constraints before expanding an experiment across an organisation.
01 / Where we can help
Where we can help
Example applications to assess in your context.
Classification and extraction
Support for reading requests, tickets and documents using agreed categories and fields. Uncertain cases go to a person instead of automatically becoming trusted data.
Assistants inside your products
Features that help users find information or prepare tasks in existing software. We restrict the tools available to the model and require confirmation for operations that need it.
02 / What the work covers
What the work covers
The actual scope is agreed during discovery.
- Use case, data constraints and evaluation criteria
- Prototype with representative examples and a baseline comparison
- Integration, access controls and error handling
- Monitoring of quality, latency and usage costs
03 / How we work
How we work
Choose a specific task
We identify users, inputs, expected output and review practices. We check whether conventional rules or structured search already solve the problem.
Measure before expanding
We build an evaluation set, including difficult cases, and compare results with a baseline. Quality, latency and cost are considered together.
Integrate with explicit limits
We introduce the system into a controlled workflow. Fallbacks, supervision and recurring checks are defined for changes in documents, models or usage.
04 / FAQ
Frequently asked questions
Do we need large amounts of data to start?
Not always. An initial document search use case can start with a small, curated collection. Other goals, such as forecasting or specialised models, require suitable historical data. We assess quality and availability before choosing an approach.
How are business documents handled?
Before connecting data, we define access, destinations, retention and the conditions of the providers involved. The choice between managed services and models in a dedicated environment depends on business constraints and must be assessed in the project.
Can the AI give incorrect answers?
Yes. Source references, evaluations on real examples and controls reduce the risk but cannot guarantee correct answers every time. We design limits and review steps that match the consequences of an error.
Can AI be added to an existing application?
Yes, after reviewing APIs, authentication and data availability. We can start with a focused feature and measure its usefulness before changing other processes.
Which task is worth testing?
Describe your starting point, the tools you use and the outcome you need. We can define the next step together.
Tell us about your project