Six outcomes, scoped and shipped.
Each engagement is named for the business outcome it produces. No hourly billing. No open-ended discoveries. Fixed scope, board-defensible measurement, and code that ships to production.
Margin Expansion
We identify the repetitive cognitive workflows in your operation — claims review, contract redlining, customer triage, onboarding QC, document analysis — and replace them with AI systems your team supervises rather than performs. Output is measured in margin points, not features shipped: senior FTEs reallocated to revenue work, volume per FTE up, marginal cost of operations down.
- ›Operating margin under pressure from headcount growth
- ›Service-ops volume scaling faster than budget allows
- ›Senior employees doing junior-level repetitive work
- ›PE sponsor pushing for EBITDA expansion in next 2-4 quarters
- ·Process-by-process automation opportunity map with ranked margin impact
- ·Built-and-deployed first automation (not a slide deck)
- ·Per-process throughput and unit-cost dashboards
- ·Quarterly margin-impact attribution
Cost Reduction
Many AI-adjacent SaaS tools are wrappers over a foundation model with a per-seat margin attached. For functions where your team has the volume and the data to justify it, we build the owned version: the contract-review system, the internal search, the customer-support assistant, the document-extraction pipeline. You stop renting; your unit economics flip.
- ›SaaS bill compounding faster than revenue
- ›BPO or outsourcing contract renewal approaching
- ›Customer-support headcount growing linearly with ticket volume
- ›Existing AI tool charging per-seat with usage-based escalators
- ·Spend audit + replace-vs-keep matrix across your AI/SaaS stack
- ·Architecture for owned replacement (model choice, infra, data flow)
- ·Built and deployed replacement, integrated with existing systems
- ·Cost-per-transaction baseline + ongoing optimization
Revenue Lift
Where most AI-for-sales tools optimize the report, we optimize the motion. We integrate with your existing CRM, sales engagement, and customer data; surface the highest-intent accounts; generate personalization at human-quality at machine-scale; and shorten the cycle between first touch and closed-won. Measured in pipeline dollars and time-to-close.
- ›Outbound team capacity-bound by manual research and writing
- ›Sales cycle dragging compared to peers
- ›Lead-quality inconsistency wasting AE time
- ›Personalization-at-scale required for ICP expansion
- ·Lead-enrichment and intent-scoring pipeline integrated to your CRM
- ·Personalized-outbound generator with brand-voice controls
- ·Deal-cycle acceleration playbooks with AI-assisted execution
- ·Pipeline-impact attribution and AE productivity reporting
Risk Reduction
AI risk is unevenly distributed: hallucination tolerance for marketing copy is high; for a regulated decision system it's zero. We scope risk by use case, build the evaluation harness that catches regressions before deploys, layer in input/output guardrails appropriate to the tier, and make every model interaction auditable. Designed for boards and compliance officers, not just engineers.
- ›Board or audit committee raising AI risk concerns
- ›Regulated-industry deployment (financial services, healthcare, legal)
- ›Existing AI feature with quality or compliance complaints
- ›Vendor or M&A diligence requires AI risk assessment
- ·Risk-tier framework per use case (regression tolerance, escalation rules)
- ·Evaluation harness with golden datasets and CI integration
- ·Production guardrails: input filtering, output validation, prompt-injection mitigation
- ·Audit log integration suitable for SOC 2, regulator inquiry, board review
Talent Leverage
Generic copilots help generically. We build the copilot that knows your codebase, your customer history, your underwriting model, your contract templates, your engineering standards. Adoption follows utility: when the copilot is actually useful for the senior IC's day-to-day work, it gets used. Designed for measurable lift in throughput, not seat-licenses sold.
- ›Senior engineers, analysts, or lawyers spending time on repetitive lookups
- ›Onboarding ramps too long; tribal knowledge bottlenecks
- ›Customer-facing teams answering the same questions repeatedly
- ›Existing copilot deployment with low adoption
- ·Role-by-role copilot scoping (what each persona actually needs)
- ·Custom retrieval-augmented assistant tied to your internal systems
- ·Integration with the tools the team already uses (Slack, IDE, CRM, etc.)
- ·Adoption and productivity-lift instrumentation
Speed-to-Market
Most cross-functional cycles are slow because handoffs are manual. We build agentic systems that perform the steps a person would perform: triage a ticket, draft a response, route to an owner, gather context, kick off a downstream job, escalate when uncertain. The agents do the boring choreography; humans review the deltas. Net effect: cycle times compressed by an order of magnitude where it counts.
- ›New features blocked by cross-team handoffs
- ›R&D-to-launch cycles measured in quarters
- ›Internal processes with high WIP and long queue times
- ›Customer-issue resolution slowed by manual investigation
- ·Workflow map: current process vs. agent-driven future state
- ·Agent system design + builds with deterministic guardrails
- ·Integration with existing tools (Slack, Linear, Jira, internal APIs)
- ·Cycle-time and WIP metrics before/after
Have a live operating problem you think AI could move?
Same-week scope review for time-sensitive engagements.