Success Stories
A rotating look at how we weave AI through complex software lifecycles while de-risking bold, AI-native ideas.
Cycle time reduction after SDLC-wide AI copilots.
Requirements, coding, QA automation, release ops.
Time to de-risk a romance-scam cybersecurity concept.
Sportstech platform modernised with AI-native SDLC
We partnered with a regulated sportstech operator to embed AI into every step of their software development lifecycle, orchestrating spec-driven development through Linear, fine-tuning Cursor on proprietary services, and codifying guardrails for high-stakes releases while QA automation finishes validation.
Highlights
- Unified AI workspace tuned to their domain-specific microservices.
- Spec-driven development orchestrated inside Linear keeps product, engineering, and QA aligned on every increment.
- Telemetry ensuring every suggestion stays compliant with internal controls while QA automation pilots extend coverage.
Deliverables
- Cursor fine-tuning pipeline aligned to the company’s code conventions and architecture patterns.
- AI-assisted requirements templates that convert betting concepts into executable specs.
- In-progress automated QA harness combining synthetic data generation with regression packs, currently being validated with QA leads.
- Deployment guardrails so AI-authored code moves through review, testing, and release safely.
Results
- 50% faster delivery velocity across prioritized squads.
- QA automation rollout in progress with pilots covering mission-critical journeys.
- Consistent developer adoption because workflows stayed inside existing tools.
Timeline
Discovery
Shadowed product, engineering, and compliance rituals to map where AI could remove friction.
Co-creation
Fine-tuned Cursor, authored prompt libraries, and paired with leads to validate outputs.
Scale
Rolled out enablement playbooks, observability, and continuous tuning cadences.
Romance-scam intelligence for an investor thesis
An early-stage investor asked us to stress-test a concept that detects romance scams before money moves. We combined ethnographic research, data partnerships, and lightweight prototypes to decide whether to greenlight the build.
Highlights
- Mapped the human, technical, and regulatory attack surface in under six weeks.
- Rapid experiments using LLMs plus behavioral graphs to classify offender patterns.
- Investment memo quantified TAM, tech feasibility, and phased go-to-market.
Deliverables
- Threat model and opportunity map covering dating apps, messaging, and payment rails.
- Signal catalogue blending scraped intelligence, partner datasets, and user interviews.
- Prototype scoring engine that ranks romance-scam risk in near real time.
- Validation report with KPIs, budget envelopes, and partnership roadmap.
Results
- Investor confidence to proceed with a staged build and initial design partners.
- Clear differentiation narrative anchored in novel behavioral signals.
- Risk controls outlined for privacy, consent, and regional regulations.
Timeline
Sensemaking
Gathered qualitative interviews, dark-web intel, and incident data.
Experimentation
Built lightweight models to test detection precision vs. false positives.
Decision
Packaged insights into an investor memo with go/no-go recommendation.
Let’s design your next success story.
Whether you need SDLC transformation or high-velocity research, we can co-create AI initiatives that respect confidentiality and deliver measurable impact.
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