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The mass tort playbook
Codify qualification and audit logic, deploy agent-driven validation, and route exceptions cleanly — so clean cases move automatically.
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Impact Velocity Studio
Playbook

How to build AI agents for mass tort case qualification & operations.

Accelerating case delivery · reducing cycle time · improving quality · scaling without increasing headcount.

20 min read
Adrian Cole · CEO & Chief Product Strategist
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◆ · Executive summary

Mass tort operators are hitting a new kind of ceiling.

For more than a decade, competitive advantage in legal marketing was won in acquisition. Firms competed on cost-per-lead, media buying, intake scripting, and channel arbitrage. The question was how to generate more qualified traffic at lower cost.

That question has changed.

Acquisition systems have matured. Intake tech stacks are stable. Marketing automation has compressed response times. The new constraint isn't volume — it's what happens after the lead enters the system.

◇ What this playbook covers

A working framework for defining and architecting AI agents across mass tort case operations.

You'll walk away with the artifacts an operator needs to codify qualification and audit logic, deploy agent-driven validation, and route exceptions cleanly — so clean cases move automatically and reviewers only touch the work that actually needs judgement. The benefits compound:

Cycle time down
Clean cases stop waiting in reviewer queues. Delivery velocity rises as they move through automatically.
Quality up
Logic enforcement removes interpretation variability. Validation is consistent across reviewers, shifts, and torts.
Scale without hiring
Headcount decouples from intake volume. ~90% of audit load auto-resolves, freeing reviewers for exception work.
Bottlenecks removed
Audit stops being the constraint between intake and delivery. The business scales to the shape of the market, not the hiring pipeline.
◇ How this playbook is laid out

Eleven chapters. Two interactive tools.

Chapter 01
Current limitations
What's limiting mass tort ops today — and why the constraint has moved from acquisition to audit.
Chapter 02
Audit is the limit
Intake scales exponentially, audit scales linearly with headcount. The gap is where margin goes to die.
Chapter 03
The mass tort pipeline
Seven stages from acquisition to delivery — and where agents plug into each one.
Chapter 04
Where audit breaks
Five recurring failure modes of manual audit — architecture problems, not talent problems.
Chapter 05
Agent-driven audit
The reframe: treat audit as infrastructure, not a department. Exception-driven review replaces universal review.
Chapter 06
The playbook
Six artifacts: workflow decomposition, validation matrix, outcome framework, triggers, routing, agent persona card.
Chapter 07
Our process
How we deliver: Discovery phase (9 steps) and Build phase (7 steps), card-by-card.
Chapter 08
ROI calculator Tool
Plug in your numbers — audit minutes, monthly volume, QA rate — and see released hours, FTE, and annual savings.
Chapter 09
AI competitive advantage
Why codified qualification logic and agent-assisted validation become durable leverage across tort cycles.
Chapter 10
AI agent readiness Tool
A six-question readiness check with a three-tier verdict — Ready, Close, Foundations.
Chapter 11
How to get started
Next steps. A 30-minute discovery call: map your operation, name the bottleneck, scope the first two or three agents.
◆ · Chapter 01

The structural shift in mass tort operations.

Acquisition has matured. Audit hasn't. The next round of competitive advantage is won after the lead enters the system.

The limit is qualification integrity, validation consistency, audit throughput, and delivery velocity. As intake pipelines grow and tort portfolios diversify, operational complexity compounds. Eligibility rules evolve. Verification data sources multiply. Documentation requirements tighten. Regulatory sensitivity rises. Each new tort brings new logic, new conditions, new decision trees.

The final gate between operational effort and realised revenue is qualification and delivery operations. Firms trying to scale without redesigning this layer see the same symptoms every time:

  • Headcount pressure rising with every volume spike
  • QA labour costs growing faster than revenue
  • Delivery timelines slipping
  • Validation outcomes that don't match across reviewers
  • Margin compression hidden inside operational growth
  • Auditor fatigue, drift, and inconsistent decisions

AI agents are emerging not as experimental tools or marketing enhancements, but as structured operational infrastructure — augmenting human qualification and audit teams and standardising validation logic at scale.

◆ · Chapter 02

Acquisition isn't the limiting factor — audit is.

Intake scales exponentially. Audit capacity scales linearly with headcount. The gap between them is where margin goes to die.

In high-performing legal marketing firms, acquisition is operationally mature. Media buying is optimised. Attribution is refined. Intake scripts are tested and iterated. Response SLAs are measured in minutes. CRM routing logic is well established.

Intake volume scales fast when a tort gains traction. Downstream systems weren't built to scale at the same rate.

Acquisition · inflow
Paid media
Organic & SEO
Social & referral
Intake automation
Flows into
The bottleneck
Audit
Review & validation
Linear capacity. Every case waits for a human before it can move forward.
Passes to
Outflow
Destination
Law firms
Delivered, audited, case-ready files — the product the law firm actually pays for.

More intake volume means more structural pressure. Eligibility matrices expand as tort criteria evolve. Verification workflows pull in multiple third-party data sources. Documentation completeness standards get tighter. Duplicate detection gets harder. Disposition tracking has to stay consistent. Law firms ask for more confidence in every delivered case.

Qualification and audit teams are now protecting both revenue integrity and law-firm trust. That pressure creates drag. Without structural redesign, firms end up trying to solve scaling problems with headcount. Each new tort cycle layers on more manual review. Each volume spike needs temp hires. Each quality incident adds another QA loop.

The long-term result is margin compression disguised as operational growth.

◆ · Chapter 03

The mass tort qualification & delivery pipeline.

Seven stages from acquisition to delivery. Every one of them is agent-addressable — this playbook focuses on the audit-heavy middle.

A modern high-velocity mass tort operation runs through seven stages. Agents can plug into any of them — here's where each one fits today and how it shifts under agent-driven infrastructure.

1
Acquisition
Media buying, multi-channel arbitrage, intake forms, response SLAs in minutes.
Where agents plug in
Channel-quality feedback loops, intake copy testing, fraud signals at the edge.
2
Intake
Script-driven intake, routed to the right tort funnel. Response time under 5 minutes.
Where agents plug in
Pre-validation classifier for matter type, jurisdiction, and complexity before a human touches the record.
3
Initial qualification
First-pass reviewer checks eligibility against tort criteria. Highly interpretive, varies by reviewer and hour.
Where agents plug in
Eligibility Agent runs against the tort's criteria matrix. Pass proceeds; Review surfaces specific flags; Fail closes with a reason.
4
Verification & enrichment
Reviewer initiates identity, address, and document verification. Waits on third-party responses.
Where agents plug in
Identity Agent, Documentation Agent, and Dedupe Agent run in parallel on trigger events. Reviewers see only flagged attributes, not clean records.
5
Audit review
Universal human review before packaging. Reviewer queues drive delivery timelines.
Where agents plug in
Exception-driven review. ~90% of clean cases auto-forward; reviewers spend 100% of attention on real judgement calls.
6
Packaging & scoring
Analyst assembles the case file, scores quality, attaches documentation index.
Where agents plug in
Packaging Agent produces a scored, indexed delivery package with traceability metadata intact.
7
Delivery
File transferred to the law firm. Delivery pace set by audit queue throughput.
Where agents plug in
Clean cases deliver on completion, not on a batch cadence. Law-firm confidence compounds.

The audit-heavy middle (stages 3–5) decides which cases move forward, which get reworked, which are escalated, which are rejected, how consistently criteria are applied, and how quickly cases are monetised. Qualification and audit logic define the reliability of the whole business model. When this layer is inconsistent or slow, every downstream stakeholder feels it — which is why the rest of this playbook focuses there.

◆ · Chapter 04

Where manual audit breaks down.

Five recurring failure modes. They're not talent problems — they're architecture problems.

1 · Failure mode
Interpretation variability
Even with standard scripts, two auditors reviewing the same intake reach different conclusions. Criteria drift across shifts, weeks, and tort types.
2 · Failure mode
Duplicate validation cycles
Cases re-enter review queues when documents arrive incomplete or status transitions are unclear. The same case consumes reviewer time three or four times.
3 · Failure mode
Queuing latency
Every case waits for a human before packaging. Delivery timelines are set by the bottom of the reviewer queue, not the top of the pipeline.
4 · Failure mode
Cognitive fatigue
Auditors run the same decision logic thousands of times per week. Edge cases blur. Confidence drifts. Quality degrades by the hour, not the month.
5 · Failure mode
Logic lives in people, not systems
Every new tort rebuilds validation rules from scratch. Decision criteria live in Slack threads, tribal knowledge, and loose SOPs — not infrastructure that enforces them.

Audit and qualification teams often become the largest controllable operational expense after acquisition. The deeper issue isn't cost — it's the way the work is designed.

◆ · Chapter 05

From human review to agent-driven audit operations.

Most firms treat audit as a department. The firms that pull ahead treat it as infrastructure.

The shift to AI-powered audit operations doesn't start with technology. It starts with product strategy. The goal isn't to replace human reviewers — it's to redesign the workflow so agents do structured validation, decision logic is codified and reusable, exceptions route intelligently, and humans focus on judgement work.

In this model, agents apply predefined validation logic, structured checks, scoring, and cross-references before a human ever touches the case. Cases that clear confidence thresholds proceed automatically. Cases in defined ambiguity zones escalate to a named reviewer with a specific flag.

The system moves from universal human review to exception-driven review.

◆ · Chapter 06

The playbook. Six steps.

Tech without operational codification fails. Before agents can execute, the institutional knowledge has to become an artifact.

Each step below is a deliverable, not a meeting. Everything on this page is always visible — read straight through, or jump to the artifact you need.

6 · 1
Mapping the workflow
Every decision auditors currently make gets decomposed into explicit logic: inputs, required documents, status dependencies, pass/review/fail/reexec/skip conditions, and escalation thresholds. If logic can't be articulated clearly, it can't be automated — let alone delegated.
◇ Inputs reviewed
Intake answersTort criteria matrix fields.
Required documentsID, medical record, retainer, proof of exposure.
Identity dataSummary + component attributes from provider.
Duplicate ledgerCross-tort claimant history.
Record statusLifecycle state + transition history.
◇ Conditions evaluated
Pass conditionsEvery criterion clears confidence threshold; no categorical exclusions; no blanks in required fields.
Review conditionsOne or more criteria below threshold; partial matches on non-critical attributes; ambiguous status transitions.
Fail conditionsCategorical exclusion present; identity summary = Fail; all supporting data null.
Re-execution triggersMissing/stale upstream data; third-party response pending; dedupe in flight.
Skip conditionsCase in Rejected status; check disabled by configuration; upstream marked Not Required.
◇ Outcome
Pass Review Fail Reexecute Skip
Escalation thresholdEach Review carries a named reviewer pool per tort + failure type.
SLAConfigured per outcome; breach fires an alert, not a silent delay.
TraceabilityEvery routed output ties back to the specific criterion and confidence score.
6 · 2
The validation logic matrix
At the core of agent execution is the Validation Logic Matrix. Each validation dimension includes five elements. The matrix transforms reviewer intuition into structured decision architecture.
1
Input parameter
The field, record, or object the validation reads from.
2
Evaluation rule
The condition applied to the input — threshold, match, presence, bound.
3
Confidence score
The numeric level the rule must clear for a verdict to stand.
4
Required supporting data
The data objects or provider responses the agent reads to evaluate the rule.
5
Outcome classification
The terminal state — Pass, Review, Fail, Reexecute, or Skip — returned to the pipeline.
For example
Eligibility confirmation may require matching intake answers against predefined criteria thresholds.
For example
Documentation completeness checks may require presence and quality validation of required uploads.
For example
Verification checks may cross-reference third-party APIs and reconcile discrepancies.
6 · 3
The outcome framework
Every validation pathway resolves into one of five defined outcome states. Clear definitions prevent drift across reviewers, teams, and tort types. To ground the framework, here's what each outcome means for one specific agent — the Identity Verification Agent — running on an inbound claimant.
Worked example
The Identity Verification Agent checks a claimant's name, date of birth, and SSN against a third-party provider. The provider returns a summary — Match / Flag / Fail / Exception — plus per-attribute results. Below: how each of the five framework outcomes applies in that run.
Pass
Auto-forward
All required conditions clear the confidence bar. Case advances automatically.
Identity agent: summary = Match, name / DOB / SSN all Match. Case auto-forwards with observations logged.
Review
Named reviewer
Ambiguity. Routes to a specific reviewer pool with a specific flag — not a generic escalation.
Identity agent: summary = Flag, SSN partial-match. Partials surfaced; routes to identity-review pool.
Fail
Closed with reason
Exclusion criteria met — or all attributes blank. Closes with a machine-readable reason.
Identity agent: summary = Fail, all attributes blank. Case closes; acquisition gets channel-quality signal.
Reexecute
Upstream retry
Missing or stale data prevents a confident verdict. Agent re-runs when upstream refreshes.
Identity agent: provider returned Exception. Case parks; agent re-fires on next provider response.
Skip
Deliberate non-action
Case state makes this validation unnecessary. Recorded deliberately, not silently.
Identity agent: claimant already in Rejected — DNQ. Skip logged with reason; no provider call made.
6 · 4
Trigger mapping
Agents execute at defined lifecycle events, not in isolation. Trigger mapping keeps them reactive inside the operational flow — and defines when they automatically re-execute, turning a one-shot check into a living guardrail.
#
Trigger
Agent fired
Re-exec
1
Status change
Lifecycle status transitions to a state that requires validation.
Eligibility Agent
On refresh
2
Document upload
New required document arrives on a record.
Documentation Agent
On OCR
3
Verification mismatch
Third-party provider returns Flag, Fail, or Exception.
Identity Agent
On exception
4
Duplicate detection
Fuzzy match surfaces a linked record on an active tort.
Dedupe Agent
On ledger update
5
Data update
A source field refreshes — auto re-execution on the affected agent.
Any agent
Auto
6 · 5
Exception routing
Routing handles the cases an agent can't resolve on its own. Every unresolved output goes one of three places: a human reviewer, an automatic re-execution, or a close.
◇ Source
Agent output
The agent has returned a verdict it can't resolve autonomously.
Terminal 01
Human reviewer
Case routes to a named reviewer pool with the specific flag and expected SLA.
Terminal 02
Re-execute
Case parks on a watcher; the agent re-fires when the triggering field or API response refreshes.
Terminal 03
Close the case
Case closes with a machine-readable reason; acquisition gets a channel-quality signal.
6 · 6
Agent execution playbook — the anatomy
Every agent in the system is documented as a portable execution playbook — a persona card. Below is a sample — the schema filled in for an Identity Verification Agent to show the shape of the deliverable. The same format scales across tort types, firms, and agent generations.
AG-03 · Identity · Sample
Class · Validation
Identity Verification Agent
Checks claimant identity against a third-party provider. Routes on the summary, surfaces partials.
Runs on trigger — provider response, field refresh, or manual re-run. Returns one of Pass / Review / Fail / Reexecute / Skip. Every re-run is logged with reason and diff.
Input fields
Full name Date of birth SSN Address Document attachments
Trigger criteria
  • Claimant record enters Pre-qualified lifecycle status
  • Verification provider returns a response for an existing claimant
  • Reviewer manually requests a re-run from the record
AI agent logic
1
Read required attributes from the claimant record — full name, DOB, SSN, address.
2
Compose a verification request payload against the configured provider (e.g. Clear, LexisNexis, credit-header service).
3
Call the provider endpoint; capture the summary row (Match / Flag / Fail / Exception) plus per-attribute results.
4
Map the summary to an outcome — Match → Pass, Flag → Review (with the specific partial-match attributes surfaced), Fail → Fail, Exception → Reexecute.
5
Write back the verdict, confidence, and per-attribute diff to the record. Route to the configured reviewer pool or terminal based on the outcome.
6
Log inputs, provider response, verdict, and a diff against the prior run — reviewer-verifiable, fully traceable.
Null handling
  • All attributes blank → Fail with reason insufficient identity data.
  • One required attribute blank (e.g. SSN missing) → Review with a request-retry flag to the reviewer.
  • Optional attribute blank (e.g. address only) → proceed using the remaining attributes; note the gap in the log.
Skip conditions
  • Claimant already in a Rejected lifecycle status — skip with reason, no provider call made.
  • Identity check disabled for this tort by configuration.
  • Upstream marked Not Required (e.g. the check is already complete from a prior campaign).
Results / outcomes
Pass
Match → auto-forward
Review
Flag → reviewer
Fail
Fail → close
Reexec
Exception → retry
Skip
Not required
Automatic re-execution
  • Provider returns an updated response (e.g. Exception → Match).
  • A required input field is corrected on the claimant record.
  • Reviewer manually retries after requesting a missing attribute.

The persona card is the deliverable. When every agent in your operation has one, you're no longer running a department — you're running infrastructure.

◆ · Chapter 07

Our process — how we deliver.

Two phases. Discovery is the blueprint. Build ships the agents.

Every mass tort operation is different enough that a templated engagement fails. Every one is similar enough that the process to get to agent-driven audit operations follows the same two-phase rhythm. Here's exactly how the work moves.

Discovery phase — the blueprint.

Before we build anything, we map what you already run. Interviews, SOP review, workshops, and process audits turn tribal knowledge into an explicit workflow. The output is an agent PRD and a feasibility check — a plan you can fund, scope, and sign off on.

1
Interviews
Stakeholder interviews across intake, qualification, audit, delivery, and leadership.
Output
Interview corpus · raw insights
2
SOP review
Current SOPs, scripts, decision trees, escalation rules read line by line.
Output
Annotated SOP library
3
Workshops
Working sessions with reviewers and leads to pressure-test decision logic and edge cases.
Output
Decision logic decomposition
4
Process audits
Live-watch audit cycles end to end. Time each step. Surface hidden rework and queue drag.
Output
Process map · bottleneck heat map
5
Agent PRD
Every proposed agent written up as a full persona card: inputs, logic, results, skip, re-exec.
Output
Agent roster · PRD per agent
6
Tech feasibility
Salesforce objects, API contracts, data availability, integration surface assessed per agent.
Output
Feasibility matrix · risks
7
Proposal
Scope, sequencing, price, timeline, success criteria. One document, no slideware.
Output
Signed proposal
8
Client review & approval
Walk-through with leadership, ops, and tech. Adjust, then lock scope.
Output
Approved scope · success metrics
9
Kickoff
Build team onboarded, environments provisioned, rituals set, first sprint scoped.
Output
Sprint 0 ready

Build phase — the cycle.

Build runs as short, focused sprints. Each agent moves through the same lanes: focused discovery to nail the spec, build, QA against the matrix, user acceptance with real reviewers, internal training, release into production, then post-release support until the agent is behaving like the persona card promised.

1
Focused discovery
Per-agent deep dive: exact inputs, edge cases, error paths. Lock the persona before a line of code.
Output
Finalised persona card
2
Build
Agent implemented against the persona. Salesforce objects, API wiring, logic, skip handling, re-exec.
Output
Working agent · dev env
3
QA
Matrix-driven test suite. Every dimension has positive and negative tests.
Output
QA report · sign-off
4
UAT
Real reviewers run real cases through the agent in parallel. Agreement rate measured.
Output
UAT acceptance · agreement ≥ target
5
Internal training
Reviewer and ops training — how to read agent output, resolve each flag, what auto-routes vs escalates.
Output
Training kit · recorded sessions
6
Release
Agent promoted to production. Shadow mode first, then live. Feature-flagged per tort.
Output
Agent live in prod
7
Post-release support
Tune thresholds, handle edge cases surfaced by volume, extend to adjacent torts. Not a hand-off.
Output
Steady-state agreement · tuned thresholds
◆ · Chapter 08

What this looks like at scale.

Cycle time drops, delivery velocity rises, quality stabilises, and headcount decouples from intake volume. Run your numbers below.

When agent-driven audit infrastructure is implemented correctly, firms see structural shifts they can measure. Case cycle time drops because clean cases stop waiting in reviewer queues. Delivery velocity rises as they move through automatically. Consistency improves because logic enforcement removes interpretation variability. Reviewer capacity goes up because attention is reserved for exceptions. Headcount decouples from intake volume.

This isn't cost-cutting — it's margin expansion through operational intelligence.

◇ Volume calculator · your numbers
min / case
cases / mo
$ / hr
Auto-resolved: 90% of cases Escalated: 10% Time saved on auto-resolved: 90% of per-case time
Hours released / month
540
32,400 min · 90% of audit load
FTE equivalent
3.4
Assumes 160 productive audit hrs / FTE / mo
Annual hours released
6,480
Reclaimable for exception handling, training, tort launch
Annual ROI · cost savings
$226,800
~$18,900 / month · at $35 / hr fully-loaded
At 5,000 cases / month and 8 minutes / case, agent-driven audit infrastructure releases ~540 hours per month — roughly 3.4 FTE-equivalents — for exception handling and new-tort enablement rather than repetitive validation.

The quality and consistency gains matter more than the hours. Law firms notice. Retention goes up. Contract value goes up. Infrastructure investment amortises quickly when applied to scaled case volume — and the compounding quality gains become their own moat.

◆ · Chapter 09

Why this becomes a competitive advantage.

Audit operations become competitive infrastructure — or they become a ceiling.

Firms that own qualification and delivery infrastructure have a defensible advantage. They deliver higher-quality cases more consistently, cut rework and duplicate cycles, earn more law-firm trust, launch new torts faster, and scale volume without destabilising teams.

Firms that codify their qualification logic and deploy agent-assisted validation gain durable leverage. Firms that stay on manual review stay constrained by linear growth.

◆ · Chapter 10

AI agent readiness assessment.

Agent deployment works when validation logic is mature, volume justifies the investment, and leadership treats audit as a strategic function. Six yes/no questions.

Not every firm is structurally ready for agent-driven audit transformation. Mark each item Yes if it's broadly true of your operation today, No if it's not. Your score updates live.

1 · Volume threshold
You process 1,500+ cases / month with active multi-tort cycles.
High-volume environments get exponential returns on structured validation automation. Low-volume operations don't yet justify the infrastructure investment.
2 · Audit team structure
You have a dedicated QA layer whose headcount scales with intake.
If scaling requires hiring more reviewers, infrastructure transformation is strategically relevant — not aesthetic.
3 · Decision logic clarity
Eligibility and validation rules are documented — not lived in tribal knowledge.
If validation logic lives in people rather than systems, codification is the prerequisite, not the output.
4 · Rework & duplicates
Cases get reprocessed; duplicates surface late in the pipeline.
High rework rates signal systemic inefficiencies agents can address. This is a "yes" for room to improve, not a celebration.
5 · Delivery pressure
Law firms demand faster, more consistent delivery — or have pushed cases back.
Audit intelligence becomes strategic when delivery reliability affects partner trust and contract value.
6 · Infrastructure maturity
You run on Salesforce or a comparable CRM with structured intake data.
Agent-driven audit needs structured data environments. Spreadsheet-first operations need foundational modernisation first.
0 / 6 ready
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◆ · Chapter 11

How to get started.

The firms that move early won't just run more efficiently — they'll redefine how qualification and delivery infrastructure works across the industry.

Mass tort qualification and delivery operations are the next frontier of operational maturity in legal marketing. The opportunity isn't automation for its own sake — it's turning a labour-intensive validation checkpoint into structured infrastructure.

AI agents, deployed inside a rigorous product strategy framework, let firms accelerate delivery, reduce cycle time, improve quality, and scale without increasing headcount. The firms that move early won't just run more efficiently — they'll redefine how qualification and delivery infrastructure works across the industry.

◇ Next step
Book a 30-minute discovery call.
No pitch deck. A working session to map your operation, name the bottleneck, and surface the first two or three agents that would move the needle — whether you decide to work with us or not.
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Adrian Cole
About the author
Adrian Cole
CEO & Chief Product Strategist · Impact Velocity Studio
I'm the CEO and founder of Impact Velocity Studio — a product strategist who has spent a career building applications across industries, turning ambiguous opportunity into structured, shippable products. Impact Velocity Studio partners with mass tort operators on Salesforce to convert reviewer intuition into codified validation logic, exception-driven workflows, and agent-assisted audit delivery — scaling quality without scaling headcount.
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Impact Velocity
◇ About the studio

A full-cycle product development studio.

Impact Velocity Studio partners with both established mid-market companies and startups. We take products from ideation through to launch — product discovery, strategy, design systems, and engineering — and we leverage AI to compress the discovery-to-launch cycle. For established operators we build agent-driven infrastructure that scales without scaling headcount. For early-stage teams we turn ambiguous opportunity into a shippable first product.

AI Agent Builds
For established & mid-market companies
Codify decision logic, deploy agent-assisted workflows, and scale quality without scaling headcount. The engagement this playbook describes.
Product Discovery & Zero-to-One
For startups and growth-stage teams
Turn ambiguous opportunity into a shippable first product. Discovery, positioning, product strategy, design system, and an engineering team that can build it.
Product Growth Audits
Teardowns, friction analysis, growth strategy
A structured look at your product — where conversion is leaking, where the funnel breaks, what to build next. Delivered as a briefable artifact, not a slide deck.
Fractional Product Retainers
For founders & CPOs who need senior product leadership on tap
Ongoing strategic partnership — product leadership, discovery cycles, roadmap pressure-testing, and team coaching — at a part-time cadence. An experienced operator in the room without a full-time hire.
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