Let customers explore a meaningful task, compare a scenario and share the exact choice they made. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Shareable, versioned customer choice snapshots
What you may be seeing
- Shared configuration links change when our catalogue changes.
- Customers cannot reopen the exact options they chose.
- Staff and buyers discuss different versions of a quotation.
- A selected variant is removed and the link silently substitutes another.
- We cannot explain what the customer originally agreed to.
We let customers save and share a clear version of their choices, including the assumptions and details needed to continue later.
Implementation & verification
We create immutable snapshots of the selected configuration, labels and applicable rule version. We code shareable references that reopen the saved choice while separately checking current price, availability and eligibility. We show explicit differences when the live catalogue has changed and require a fresh confirmation before commitment. We preserve snapshot history for staff and connect the confirmed version to the quotation or order record.
How we verify the work
- Separate saved choice data from mutable product records and live commercial terms.
- Version configuration rules and preserve deleted-product labels in the snapshot.
- Show substitutions and price changes explicitly, rather than changing the saved choice.
- Accept on exact reopening, traceable confirmations and correct current-order validation.
Tools considered for this work
Delivery: backend/frontend development + versioned database records. Storage: PostgreSQL or the existing order/configuration database. References: signed/scoped links or account-bound snapshot IDs. Integration: live catalogue/pricing API (a controlled connection between applications) and CRM (a system for managing customer relationships)/order system. Validation: snapshot fixtures, catalogue revision tests and link permission tests.
02Safe self-service trial in an existing customer app
What you may be seeing
- Prospects want to try the product before committing.
- A sales demo does not let customers complete a real task.
- Trial users might access real customer information.
- The demo environment gets cluttered with previous users' work.
- People finish the trial but cannot continue into a purchase.
We create a controlled trial experience where customers can explore a defined task without affecting real accounts or business records.
Implementation & verification
We add an isolated trial workspace to the existing app with synthetic data and a deliberately limited set of actions. We code tenant separation, reset/expiry behavior and restrictions on external side effects such as real payments or emails. We provide a meaningful sample task and connect successful trial completion to an appropriate account or sales next step. We test both task usability and isolation so a convincing trial does not expose production data or create unintended transactions.
How we verify the work
- Select a valuable workflow that can run completely with safe synthetic records.
- Block production credentials/actions and verify isolation across trial workspaces.
- Make reset, expiry and upgrade behavior explicit; preserve only approved customer-created information.
- Measure task completion and qualified continuation while verifying zero production-data exposure.
Tools considered for this work
Delivery: application/backend development + environment configuration. Isolation: existing tenant model or PostgreSQL/Supabase access policies. Fixtures: synthetic seed data, reset jobs and test-mode third-party integrations. Feature control: existing feature flags or GrowthBook/PostHog. Verification: Playwright tasks, access-boundary tests and trial outcome events.
03Interactive customer scenario demonstration
What you may be seeing
- Our sales page cannot show how the service works for different customers.
- Staff repeat the same scenario demonstration on calls.
- Customers do not understand the effect of changing their inputs.
- Interactive demos look convincing but produce invented results.
- Prospects cannot share the scenario they discussed with colleagues.
We build an interactive demonstration around a realistic customer task, making the product easier to understand before committing.
Implementation & verification
We build an interactive demonstration around approved scenario inputs and a reproducible rule or simulation model. We code the interface and calculation/service calls so the outputs change consistently with the buyer's selections. We label demonstration assumptions and keep simulated outcomes distinct from actual forecasts or committed service terms. We add an appropriate share or enquiry route and verify the demo against known reference scenarios.
How we verify the work
- Choose inputs that affect the actual decision rather than decorative interactivity.
- Validate outputs against reference calculations and make model limits visible.
- Preserve shareable scenario state and an obvious path to a relevant next action.
- Measure comprehension, demonstration completion and qualified enquiry/purchase continuation.
Tools considered for this work
Delivery: frontend development + optional simulation/backend integration. Design: Figma prototype of the specific customer task. Production: existing JavaScript framework and approved calculation/rules service. Data: synthetic fixtures or approved public sample records. Verification: scenario fixtures, Playwright and completion events.
Let customers explore a real decision.
We build interactive scenarios, saved choices and controlled trials. Automation preserves the selected state; optional AI guidance explains the scenario without presenting sample results as a live commitment.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Relevance
Useful results for representative real customer queries.
Confidence
Recommendations that satisfy stated requirements.
Progress
Successful searches, trials and qualified buying actions.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Is this just for online shops?
No. Search and decision support also help customers choose software plans, technical products, services and knowledge resources. We adapt the experience to the decision.
Can AI recommend incompatible products?
It can if the system relies on generated text alone. We put compatibility, price and availability checks in application logic and use AI to explain the eligible results.
Can you guarantee visibility in AI search engines?
No. We improve the quality and consistency of your published data and supported feeds. Search platforms decide eligibility and presentation; recommendations and rankings are not guaranteed.