Add useful AI to your product.
We build search, assistants or document features into your app and test them on real user tasks.
What happens after a new customer signs up?
They create a workspace, invite their team and complete the project brief before the first review.
- Onboarding guide
- Project checklist
Sources limited to this user's access
What we build into your product
We build AI features around a task your customers already do, such as finding an answer in their documents. We connect approved data, apply each user's access rules and test the results on real examples. You can see answer quality, response time and cost as the feature reaches users.
For product teams adding a new capability or improving an AI feature customers cannot yet rely on.
- Search your data
- Assist inside your app
- Extract from documents
A useful user workflow
We choose the user task, put the feature where it belongs in your product and define when it should ask for help.
The right data and permissions
We connect approved documents, records and APIs, then apply access rules before information reaches the model.
Tests for useful answers
We build a set of real questions and failures, then compare the data search, prompts and models against those same cases.
A measured rollout
We set speed and cost targets, release to a small group first and monitor the answers users receive.
Example workflow
Answers from the right documents
For a product that helps customers find answers in their own knowledge base.
Ask a question
A signed-in customer asks about a policy, process or document.
Find allowed documents
Search retrieves relevant passages from documents that customer may access.
Answer with sources
The feature cites its evidence or explains when it cannot answer.
What we verify: The answer uses the right documents and never pulls in another customer's data.
Choose the simplest approach that fits
Choose the simplest approach that fits
We choose the method from the task and the data, then test it before scaling.
Answer questions over changing documents
- A useful starting point
- Retrieval with source references
- What needs checking
- Whether the right evidence reaches the model
Extract fields from a document
- A useful starting point
- Structured extraction
- What needs checking
- Accuracy, validation and handling missing fields
Query business records
- A useful starting point
- A controlled tool or database interface
- What needs checking
- Permissions and the meaning of the returned data
Follow a fixed decision rule
- A useful starting point
- Conventional application logic
- What needs checking
- Whether a model adds value at all
| The task | A useful starting point | What needs checking |
|---|---|---|
| Answer questions over changing documents | Retrieval with source references | Whether the right evidence reaches the model |
| Extract fields from a document | Structured extraction | Accuracy, validation and handling missing fields |
| Query business records | A controlled tool or database interface | Permissions and the meaning of the returned data |
| Follow a fixed decision rule | Conventional application logic | Whether a model adds value at all |
How we work together
Choose the job
We agree on the user task and collect examples of answers that help, fail or should never be given.
Connect and evaluate
We connect the permitted data and compare approaches against the same examples before choosing one.
Integrate into the product
We add the feature to your app with access checks, a fallback when it cannot answer and usage limits.
Roll out and monitor
We release gradually, review user results and turn failures into checks for the next change.
Common questions
Can we add AI without rebuilding our product?
Can we add AI without rebuilding our product?
Usually. We look for an integration point that fits your existing application and data model. A rewrite is only proposed when the current foundations prevent the feature from working safely.
Do we need retrieval or a large context window?
Do we need retrieval or a large context window?
We test both against your task. A small, bounded document set may fit directly in context; large or frequently changing collections often need retrieval.
How do you keep one customer's data away from another?
How do you keep one customer's data away from another?
The data access layer applies the user's permissions before content is sent to the model. We test those boundaries with representative accounts and records.
Which AI model will the feature use?
Which AI model will the feature use?
We compare models on your examples, response-time needs, data requirements and running cost. The choice follows the results rather than a default vendor preference.
Have an AI feature in mind?
Show us the workflow and the data behind it. We'll help you choose what to build.
or reach us at hello@phaseahead.ai