AI products and features
Embedded copilots and intelligent search
Put grounded assistance inside the product context where users already inspect, decide, and act.
Primary buyer: SaaS product and engineering teamsThe real constraint
The feature must create user value while containing model latency, cost, failure, and product risk.
How we approach it
We design the user decision, model boundary, evaluation, application surface, and rollout as one product system.
How we prove it
A paid prototype measures user completion, model quality, latency, cost, and recovery before production scope.
Production system
AI products and features
Intelligence embedded in a useful product workflow, not isolated behind a chat box.
- 01Product and workflow discovery
- 02Model and tool orchestration
- 03Evaluation and failure handling
- 04Application UX and permissions
- 05Deployment, telemetry, and rollout
Typical fit
Faster task completion without a disconnected generic chatbot
FAQ
Questions before a pilot
These answers describe how we normally work. Only a signed agreement creates commitments. Website enquiry terms
01Can you work with a product that already exists?
Yes, and that is the normal case for this kind of work. Adding a model to a product with real users is a different problem from starting clean: there is an existing data model, an existing permission system, and users who will notice a regression immediately. We treat those as constraints from the first week rather than things to refactor around later.
02How do we know the model is good enough before committing to a build?
We build an evaluation set from your real cases and measure against it before the production work starts. That produces a number attached to a decision, good enough to ship, good enough with human review, or not good enough yet, instead of a demo that happened to work three times in a row.
03What if the model turns out not to be the answer?
Then we say so, and discovery has done its job. Some of the most useful outcomes of this phase are finding that a rules engine, a better interface, or a fixed data problem gets you further than a model would. You will have paid for a bounded piece of work instead of a year of production build on a bad premise.
Service focus
Assistance inside the product, not bolted onto it
Help belongs at the point of the decision, not in a chat box beside it. We put the assistant where the user is already deciding something, wire it to your permission model, and make every answer show its sources. If it cannot answer, it says so instead of inventing something.
- Built for
- SaaS product and engineering teams
- Designed to achieve
- Faster task completion without a disconnected generic chatbot
What you get
- Grounded answers with visible source evidence
- Permission-aware retrieval over your own content
- Task-completion measurement against today's baseline
- In-product interface and actions, not a separate chatbot
Need a different system?
Not quite your solution?
Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.
Your version will have different constraints.
Bring the workflow, current system, available evidence, and the assumption you trust least. We will identify the smallest useful next step.
Discuss this system