AI turns ambiguity into software faster than ever.
A product decision can become a journey, a spec and a code change before every team has understood it the same way.
Only before renewal.
Any time from the account page.
Immediate plan change.
Builds a cancel-then-reactivate flow.
Everyone moves fast. Each one builds something different.
Small gaps in interpretation create costly delivery problems.
Wrong features
The implementation solves a different problem.
More rework
Teams fix decisions once development is underway.
Slower sign-off
The same requirement gets clarified several times.
Product drift
What ships moves away from the original intent.
Product knowledge is scattered across too many tools.
Intent, decisions, rules and priorities live in documents, tickets, mockups, meetings and one-to-one chats. No system connects them and checks them continuously.
Notion
Jira
Figma
Google Sheets
Confluence
Google SlidesIt is a lack of consistency.
A shared Product knowledge base. Connected and continuously verified.
Lyriks links every key product decision into a structured model that teams and AI agents can understand and act on.
Product Truth
Connect personas, needs, features, rules, priorities and roadmap.
Specification Readiness
Check that every requirement is clear and complete before delivery starts.
Consistency Check
Detect contradictions, missing information and incompatible decisions.
Select an item to see its dependencies. When a rule changes, its impacts are highlighted.
What you actually run inside Lyriks.
Not promises: the real capabilities you operate, from product intent to consistency control, all in one place.
List your personas.
Describe who you serve: roles, needs, usage context. Each persona becomes an anchor linked to needs and journeys.
Define your core features.
Organize the product into families and features, linked to the needs they cover. Scope becomes explicit, not implicit.
Set priorities and the release roadmap.
Make trade-offs (must, should, later) and assign features to releases. The roadmap reflects traceable decisions, not a frozen list.
Describe your business rules.
Formalize the rules that govern product behavior. They become verifiable, linked to the features and journeys they affect.
Check Design, Functional and Data consistency.
Make sure the experience, the functional behavior and the data stay aligned with intent, and catch gaps before the build.
From fragmented knowledge to shared understanding.
- Intent scattered across tools
- Different interpretations per team
- Missing rules found during delivery
- Decisions hard to trace
- Rework once development has started
- A shared product model
- Connected, traceable decisions
- Gaps detected before delivery
- Teams aligned on priorities
- Less drift and less rework
Product consistency that is visible and measurable.
Build the right product from the start.
- Specification readiness score
- Contradictions detected
- Missing rules identified
- Decisions traced
- Impacts visualized
- Gaps resolved before the build
Fewer specification gaps
Ambiguities, missing rules and incomplete scenarios identified before development.
Faster product validation
Less time spent clarifying, reviewing and approving specifications.
Less product rework
Fewer corrections caused by misunderstood or incomplete requirements.
Target outcomes measured during pilot.
A clearer product before execution begins.
Faster decisions
Every team sees the same context.
Fewer wrong features
Ambiguities are resolved earlier.
Less rework
Product gaps are caught before development.
Stronger alignment
People and AI share the same understanding.
Clarify product intent before AI turns it into software.
Give Product, Design, Development and AI agents the same understanding of what to build.
AI generates screens faster than it understands the experience.
A screen can look fine on its own yet break the overall journey. AI produces interfaces quickly, but it does not always know the business rules, the intermediate states, the exceptions or the decisions already made.
Plans edits right inside the checkout flow.
Pictures managing it from the account area.
Implements only the address form.
Generates a new screen without validation, errors or history.
Every screen works. The journey breaks.
A small break in the experience becomes a product problem.
Forgotten states
Errors, loading, confirmations or exceptions go undesigned.
Fragmented journeys
Screens follow different logic from team to team.
Misrendered rules
The interface does not always reflect the expected functional behavior.
Late rework
Inconsistencies surface during development or QA.
The experience is designed across separate tools.
Personas, journeys, screens, business rules and components live in separate spaces, with no continuous link between intent and interface.
NotionFigJamFigmaConfluenceJiraIt is making sure they tell the same experience.
Connect the journey, the interface and the expected behavior.
LYRIKS builds a continuous view of the experience and checks that every screen, every state and every interaction stays consistent with the product.
Journey Truth
Links personas, needs, journeys, steps and screens.
Experience Coverage
Spots uncovered states, errors, exceptions and scenarios.
UX to Rule Sync
Checks that interactions respect business rules and functional behavior.
Select a link to see its role in the experience.
What you actually design inside Lyriks.
Not promises: the real capabilities you operate, from the user journey to functional consistency control, all in one place.
Map your journeys.
Visualize the full user journey. Each journey links steps, screens, decisions and alternative scenarios.
Connect your screens.
Tie each screen to its context: persona, need, journey step and functional rule. Traceability becomes native.
Check your coverage.
Spot missing states and scenarios. Lyriks detects errors, exceptions, confirmations and alternative paths that were never designed.
Sync your design system.
Check that the right components are used. Each screen is compared against the design system's components, variants and usage rules.
Verify functional consistency.
Compare the interface to the expected behavior. Lyriks flags gaps between what the screen allows and what the product rules permit.
From isolated screens to a continuous journey.
- Journeys scattered across several tools
- Screens designed without the big picture
- Missing states found late
- Design system applied unevenly
- Functional rules checked manually
- Journeys, screens and rules connected
- A complete view of the experience
- Missing states caught upfront
- Consistent, reusable components
- Interactions verified before development
A consistent experience, visible and measurable.
Design complete journeys before they become expensive fixes.
- Full coverage of user journeys
- Screens linked to the right steps
- Missing states identified automatically
- Interactions mapped to business rules
- Components compliant with the design system
- Functional gaps detected before the build
Fewer missing states and edge cases
Incomplete screens, errors and alternative paths identified before development.
Faster design validation
Less time spent aligning Product, Design and Engineering on expected behavior.
Less UX rework
Fewer corrections caused by broken journeys, inconsistent interactions or missing states.
Target outcomes measured during pilot.
A more complete experience before development.
More complete journeys
Every step, state and exception is accounted for.
Fewer breaks
Screens and interactions follow a shared logic.
Faster sign-off
Product, Design and Engineering share the same view.
Less rework
Inconsistencies are caught before development.
Design every screen as part of the same journey.
Connect personas, needs, screens, interactions and functional rules into one consistent experience.
Give AI the right context, then verify what it builds.
A ready spec, targeted context and continuous control to keep the implementation faithful to product intent.
AI codes fast, but not always with the right understanding.
An AI agent can produce a technically correct implementation while ignoring a business rule, an expected behavior or an existing dependency. The developer then has to keep re-prompting, check assumptions and redo what was generated.
The change should take effect at the next renewal.
The change can be applied immediately.
A new cancel-then-reactivate flow is generated.
Proration logic already exists, but it is ignored.
The code works. The product does not do what was expected.
A wrong understanding quickly turns into rework.
Repeated prompts
The developer rephrases the same request several times to get a usable result.
Functional hallucinations
The agent invents rules or behaviors that are not in the spec.
Unneeded code
Components, dependencies or logic are added without need.
Longer review
Time saved during generation is lost during review and fixing.
The useful context is fragmented and hard to pass on.
AI agents have no reliable view of product intent, functional rules, technical dependencies and decisions already made.
NotionJiraFigmaConfluence
Slack
GitHubIt is the absence of the right context at the right time.
Prepare the request, target the context, verify the result.
Lyriks frames the AI-assisted development cycle before, during and after implementation.
Definition of Ready
Checks that the spec is clear, complete and actionable before development.
Context Precision
Supplies only the product, functional and technical context the task needs.
Build Verification
Compares the implementation against the original spec and flags gaps.
Select a link to see its dependency. When a rule changes, the affected scenarios, components and checks are highlighted immediately.
What you actually operate inside Lyriks.
From readiness control to build verification: a complete cycle to develop with AI without losing the thread of intent.
Check readiness.
An automatic analysis surfaces missing information, ambiguities and undefined rules before the build.
Simulate behaviors.
Happy paths, errors and edge cases are simulated before implementation.
Prepare the context.
Lyriks selects only the information the requested task needs, nothing more.
Connect your LLM tools.
The developer or the agent pulls the useful context straight from their usual tools.
Check the implementation.
Lyriks identifies missing behaviors, unrequested additions and functional gaps.
From copy-pasted context to a guided, verified cycle.
- Incomplete spec at the start
- Context copied by hand into prompts
- Many iterations before a usable result
- Business rules forgotten during implementation
- Gaps found in review or QA
- Readiness verified before development
- Targeted context generated automatically
- Agents guided by a consistent understanding
- Behaviors simulated before the build
- Implementation checked after generation
AI-assisted development that is visible and measurable.
Give AI the right context and get production-ready code faster.
- Specification readiness score
- Ambiguities identified before development
- Scenarios simulated before the build
- Context actually sent to the agent
- Gaps detected after implementation
- Traceability across rule, behavior and code
Fewer prompt iterations
Less time spent reformulating requests and correcting misunderstood context.
Faster implementation validation
Shorter review cycles between generated code, specifications and expected behavior.
Less functional rework
Fewer corrections caused by missing rules, overlooked dependencies or unintended behavior.
Target outcomes measured during pilot.
More fidelity to intent, less friction.
Fewer prompts
The agent gets a clearer request and tighter context from the start.
Fewer hallucinations
Expected behaviors and business rules are explicitly defined.
Faster reviews
Gaps are flagged before the developer has to check everything by hand.
A more faithful implementation
The code stays aligned with product intent and the original spec.
Give your AI agents the context they are missing.
Prepare each request, guide the implementation and verify the result in one consistent cycle.
Move several teams forward without multiplying misalignment.
A shared view of specs, dependencies and risks to coordinate human teams and AI agents at scale.
AI speeds up every team, but not necessarily the organization.
When several teams and AI agents work in parallel, each can move fast while making decisions that are incompatible with the others. The gaps often appear too late, during review, integration or QA.
Create a new plan and new eligibility rules.
Design a new upgrade journey.
Change how subscriptions and payments work.
Updates the screens without applying the existing billing rules.
Everyone moves forward. The product does not converge.
Individual speed becomes a collective problem.
Forgotten dependencies
A team discovers too late that it depends on unfinished work.
Cross-team conflicts
Two teams change the same behavior in different ways.
Late coordination
Trade-offs happen once the work is already underway.
Eroded margin
The time saved upfront is absorbed by rework.
Production is parallelized, but convergence is not.
Teams have their own tools, decisions and context. What is missing is a shared view of work in progress, dependencies and cross-impacts.
ProductboardJiraFigmaConfluenceSlackGitHubIt is a lack of convergence.
A shared view to coordinate, anticipate and decide.
Lyriks connects specs, decisions, dependencies and work in progress to catch risks before they block delivery.
Cross Expertise Check
Detects inconsistencies across Product, Design, Functional, Data and Engineering.
Team Sync
Spots forgotten dependencies, duplicates and incompatible decisions.
Conflict Radar
Anticipates potential conflicts across work, components and teams.
Select a link to see its dependency. A delivery map connects goals, work items, teams and dependencies, and surfaces risk areas before integration.
What you actually operate inside Lyriks.
From readiness to convergence: a consolidated view to coordinate several teams and AI agents without losing overall consistency.
Visualize readiness.
The manager instantly sees which topics are complete, incomplete or at risk.
Map the dependencies.
Each feature is tied to the teams, components and decisions it depends on.
Detect misalignment.
Lyriks identifies differences in interpretation across Product, Design, Functional and Engineering.
Anticipate conflicts.
Concurrent or incompatible changes are flagged before the merge.
Track convergence.
The manager tracks the gap between what was planned, produced and actually integrated.
From scattered streams to a synchronized delivery map.
- Different specs from team to team
- Dependencies discovered during development
- Decisions hard to track down
- Conflicts visible only at integration
- Coordination based on extra meetings
- A shared understanding of scope
- Dependencies visible before kickoff
- Connected, traceable decisions
- Conflicts anticipated before integration
- Trade-offs guided by a consolidated view
Multi-team delivery that is visible and measurable.
Scale parallel execution without scaling coordination costs.
- Readiness score per topic
- Dependencies identified across teams
- Misalignment detected across disciplines
- Potential conflicts flagged
- Decisions and trade-offs traced
- Gaps measured before integration
Fewer late integration conflicts
Dependencies, overlapping changes and incompatible decisions identified earlier.
Less coordination time
Fewer meetings, clarification loops and manual alignment across teams.
More predictable delivery
Better visibility on readiness, dependencies and convergence risks.
Target outcomes measured during pilot.
More predictable delivery, better protected margin.
More predictable delivery
Risks are visible before they become blockers.
Less late coordination
Dependencies and misalignment are spotted earlier.
More controlled parallel work
Teams move at the same time without losing overall consistency.
Better margin protection
Fewer conflicts, less rework and fewer delays.
Make teams converge as fast as AI accelerates them.
Give every team the same understanding of goals, dependencies and risks.
AI raises output, but not automatically profitability.
Teams adopt new tools, models and AI agents to save time. But without consolidated tracking, the gains can be absorbed by licenses, consumption, rework, integrations and uncontrolled usage.
Expects a measurable cut in production costs.
Negotiates licenses with no view of real usage.
Adopt several tools based on local needs.
Expects better time-to-market and margin.
Everyone invests in AI. No one measures the same value.
Visible gains can hide diffuse costs.
Spend hard to attribute
Costs are not linked to the features or projects they belong to.
Redundant tools
Several solutions cover the same needs across teams.
Unmeasured rework
Time saved generating is lost fixing.
Margin hard to protect
The claimed productivity does not clearly show up in results.
Usage, cost and results are tracked separately.
Finance data describes what was spent. Technical tools show what was consumed. Delivery teams track what was produced. No system links this information precisely.
JiraIt is the inability to link that cost to the value created.
Link AI usage to costs, rules and results.
LYRIKS consolidates practices, consumption and performance to make the economic impact of AI visible and verifiable.
AI Policy Tracking
Tracks AI policy compliance by team, tool, project and use case.
Cost Attribution
Attributes costs to features, people, teams, agents and models.
AI Business Performance
Links AI usage to lead time, rework, quality and margin.
Select a link to see its role in the AI value chain.
What you actually run inside Lyriks.
Not promises: the real capabilities you operate, from mapping AI usage to measuring the value produced, all in one place.
Map your usage.
Identify who uses what and why. Each tool, model and agent is linked to a team, a project and a use case.
Track AI policy.
Check compliance with internal rules. Usage is compared against approved models, accessible data and expected practices.
Analyze the costs.
Attribute each spend to the right scope. Licenses and consumption are broken down by feature, team, user and agent.
Identify best practices.
Compare usage and its results. Lyriks highlights the tools, teams and methods that produce the best results.
Measure the value produced.
Link AI investment to performance. Costs are compared against the gains observed on delivery, rework, quality and margin.
From diffuse spend to attributable value.
- Costs spread across several vendors
- Usage hard to identify precisely
- Policy compliance checked manually
- Productivity gains based on claims
- ROI computed from broad assumptions
- Usage and costs consolidated
- Spend attributed to the right scopes
- Compliance tracked continuously
- Best practices made visible
- Value measured per feature and team
The economic impact of AI, visible and measurable.
Turn AI productivity into higher margins and measurable profitability.
- Tools and models actually used
- AI policy compliance per team
- Risky or out-of-bounds usage
- Costs attributed per feature
- Highest-performing practices
- Impact on lead time, rework and margin
Savings on token consumption
Less unnecessary context, fewer repeated prompts and more targeted AI usage.
Lower rework costs
Less budget lost to late corrections, misalignment and integration issues.
Better project margin
More of the productivity gain reaches the bottom line.
Target outcomes measured during pilot.
AI managed like an investment.
Full financial visibility
Every cost is linked to a use, a team and a result.
More reliable governance
Risky or off-policy practices are spotted quickly.
Better-judged investments
Tools and models are compared on their real value.
Better protected margin
Speed gains are no longer quietly absorbed by rework and hidden costs.
Make AI a measurable investment, not a diffuse cost.
Connect usage, cost, compliance and performance in one view.