About Sol Helps
Make user uncertainty
visible enough to act on.
Sol Helps combines an embedded AI help experience with an evidence dashboard. The assistant helps users in the moment; the dashboard preserves the questions they submit, relevant page context, and recurring patterns so teams can investigate where guidance may be unclear.
Evaluating privacy or implementation first? Review trust and security
The idea
The investigation workflow is the product.
Sol Helps helps teams investigate what customer questions reveal about unclear products, guidance, and experiences. The embedded assistant is the primary live evidence source, while limited existing-evidence pilots support selected teams with historical question sets.
Evidence first
Keep the original question behind every summary.
Sol Helps is designed around inspectable evidence. Teams can review what a user submitted, where it was asked, how the assistant responded, and whether similar questions recur before deciding what the pattern means.
Bounded claims
Treat signals as evidence, not automatic truth.
A repeated question can point to weak guidance, unclear wording, onboarding friction, or a product problem. Sol Helps helps teams investigate those possibilities without claiming to identify every root cause or prove business impact by itself.
Privacy and control
Collect deliberately, with clear boundaries.
Conversation logging is consent-aware and controlled by plan and configuration. Access is scoped by workspace and assistant permissions, and teams remain responsible for deciding where collection is appropriate.
Founder perspective
Why I built Sol Helps
Teams can usually see that a page is underperforming, a workflow is creating tickets, or documentation is not landing. What they often cannot see is what the user was trying to understand at the moment they needed help.
Analytics explains reach and behaviour. Support systems manage cases. Research creates deeper understanding at selected moments. Sol Helps was built to add another kind of evidence: the user’s submitted question, preserved with enough context for a team to investigate it later.
The aim is not to replace those systems or turn every question into an automatic diagnosis. It is to make repeated uncertainty visible, inspectable, and easier to translate into better documentation, product wording, onboarding, or support decisions.
“The product should help teams see uncertainty without pretending it already knows the answer.”
Founder
Jackson MillerProduct-focused web engineer working across digital experience, documentation systems, telemetry, analytics, and privacy-aware customer-facing tools.
LinkedIn profile →What the product does
One interaction, two useful outcomes.
Sol Helps can assist the person asking the question while preserving evidence for the team responsible for improving the experience.
The widget collects the signal
Users ask for help through an inline or floating assistant embedded on a customer-facing page.
The dashboard preserves the evidence
Teams can inspect submitted questions, assistant responses, page or path context, feedback, unanswered state, notes, and review status.
Themes make recurrence visible
Broad rule-based themes, counts, rates, representative evidence, and context rollups help teams see where patterns may be forming.
People still make the diagnosis
Sol Helps supports investigation and prioritisation. It does not automatically identify the exact stale paragraph, broken concept, or product decision.
Existing systems still matter
Analytics, support tools, research, documentation platforms, and experiments provide evidence Sol Helps does not replace.
Focused deployments create better evidence
The strongest pilots begin on one meaningful surface with enough relevant knowledge and a team able to act on what it learns.
Where it fits
Sol Helps complements the systems you already use.
No single signal explains customer behaviour on its own. Sol Helps is most useful when its conversation evidence is interpreted alongside other operational and behavioural data.
Sol Helps can show
- What a user submitted through the embedded help experience.
- The page or path associated with the interaction when available.
- Whether the answer drew a follow-up, negative feedback, or remained unanswered.
- Whether broad themes recur across a selected period.
Other systems still provide
- Full traffic, funnel, cohort, and conversion analysis.
- Human support workflow, customer history, and case resolution.
- Deep research into needs, motivations, and mental models.
- Controlled evidence that a specific change caused an outcome.
Trustworthy product decisions usually come from combining evidence, not asking one dashboard to explain everything.
Inspect the evidence before installing anything.
Explore a clearly labelled example report using fictional data, or request a Product Clarity Audit for one public product page, flow, or small documentation set.
Ready to configure an assistant instead? Start free.
A practical starting point
Start with one evidence path worth understanding.
Focus the assistant on a meaningful live surface, or use the Product Clarity Audit to ask whether a bounded historical question set is suitable for an assisted private-beta pilot. The two evidence workflows remain separate today.
For plans and evidence limits, see pricing.
Made with ♥ by Sol Helps