Voice AI replaced manual phone screening at a healthcare scale-up.

• Screens applicants within minutes of applying, then auto-books keepers with recruiters
• Pushes transcripts and sentiment to the ATS — time-to-hire 5X faster

A composite-key engine cleans dirty job data that defeated naive ingestion.

• PostgreSQL warehouse with row-level security replaced spreadsheets
• Defect and downtime now tracked at shift level, not monthly

Built on the plant's own sensor and CMMS data, surfaced in supervisors' existing routine.

• Daily failure-probability rankings cut downtime 14.2% to 8.7%
• $1.2M saved in year one; false positives down 40%

Trust is enforced per run, not per project: three layers gate each generation.

• Eval layer checks accuracy, PII-safety, and authorization each run
• Adversarial reviewer model flags inconsistencies before sign-off

Governance and audit trails were architected from inception, not bolted on.

• Natural-language layer returns consolidated answers with no SQL
• Company-level scoping keeps consolidation inside access limits

Grounded in Cando's own assessment history rather than generic retrieval.

• Automated drafts run at under $0.05 each
• Live voice capture freed 20+ expert hours per assessment

One reusable architecture, re-fit to each site's own data instead of rebuilt.

• Rollout fell from 6 weeks to 3 by the third plant
• Downtime dropped 14% to 8.8%, saving $2.7M a year

Auditability designed in: no code is final until it links to source.

• Three-model pipeline with human gates blocks hallucinated codes
• Every code traces to source chart text with confidence scoring

Retrieval-augmented generation grounded every recommendation in Superintelligent's own tool catalog.

• Embeds queries and retrieves matches from a Pinecone index
• Delivered hundreds of personalized tool recommendations

Five sequential agents split the work into classify, enrich, discern, and judge, each with confidence scoring.

• Categorizes 1–2M records a month at 85%+ accuracy
• Maps to a 10,000-category taxonomy in real time

A zero-write layer keeps the live ERP untouched while consolidating every domain.

• Read-only feed replaced scattered exports with one governed store
• Daily dashboards for volume, yield, and orders used by named owners

Research was deliberately separated from judgment to cut bias, mirroring how analysts actually work.

• Decision-ready trend reports in under 35 minutes
• About $3 per report, ~80% verdict consistency on re-run

Equivalence is proven with evidence, not assumed: production data drives it.

• Real production data replayed through legacy and migrated systems
• Regressions surface before cutover, hitting the ECC support deadline

A read-only agent with human review kept engineers in control of every change.

• Live metadata context drove up-to-10x faster configuration
• Temperature tuning (1.0→0.4) cut errors ~50%

A multi-agent design separated interviewing, drift detection, and evaluation to keep quality high at scale.

• Interviewed 150 employees in two weeks for $500
• Ran thousands of interviews in its first month live

Making the pipeline measurable came first; only then did targeted experiments improve it.

• Seven-metric evals cut endpoint hallucinations 26% to <1%
• Prompt caching cut cost ~25% (about $6) per run

A five-step workflow turns raw API docs into reviewable connector drafts, keeping a human in the loop.

• Crawls docs and pre-fills config — builds 10x faster
• Connector volume rose after the AI Assist launch

Sales and marketing systems unified to steer spend by LTV:CAC.

• Gong, Salesforce & HubSpot in one Connect environment
• Reverse ETL feeds LTV:CAC insights to ad platforms; lower cost/account

One reporting engine plus a CDP that activates audiences into ad platforms.

• Reverse ETL pushes custom segments to Meta & Google; sales +30%
• AskCorral AI querying alongside ROAS/conversion +39%, AOV +14%

Hundreds of disparate sources unified for non-technical staff.

• Tessitura, Delighted & the Explorer app in one analytics layer
• Staff self-serve NPS, donations and visitation drivers on demand

Fragmented e-commerce channels consolidated into one attribution view.

• GA/GA4 blending + attribution ties LTV:CAC to first product and campaign
• Unified reporting cut acquisition cost and lifted marketing ROI

Self-serve analytics for every team — no data-engineering hires.

• Mixpanel, AppsFlyer & GA unified into one governed reporting layer
• Reverse ETL flags churn-risk patrons and automates growth actions

A Phase 1 'Foundation' build paired brand and go-to-market groundwork with a lightweight AI layer.

• Visual identity, messaging, ICP and pricing validation delivered
• AI website chatbot qualifies leads, wired into a HubSpot CRM setup

A four-week 'Step Zero' study proved feasibility before any build was funded.

• Local vision models validated 98% accuracy, zero images sent to cloud
• 3 independent CIOs approved a 20-week roadmap across 8 systems

QuantFi ran a full unit-economics review for a fast-growing D2C wellness brand, building a fully burdened contribution-margin model across COGS, fulfillment, marketing, fees, returns, and CAC. It lifted gross margin 15%, cut wasted spend 20%, and raised EBITDA margin 20%.

QuantFi implemented NetSuite for a high-growth CPG brand running U.S.–Mexico warehouses, with multi-subsidiary, multi-currency operations and Shopify and 3PL integrations. It delivered 100% SKU-level visibility and cut the monthly close from 15+ days to under five.

QuantFi built a dynamic deal model for a PE sponsor underwriting a complex carve-out, flexing 15+ capital structures and three operating scenarios in real time with return waterfalls and a stranded-cost model. The sponsor went from LOI to a diligenced model in two weeks.

QuantFi turned an early-stage AI co-pilot startup's usage economics into an investor-grade financial model in under three weeks, designing hybrid subscription-plus-token pricing and 5+ capital-raise scenarios. The startup adopted token pricing pre-launch.

QuantFi ran a full sell-side process for a Midwest industrial business: an integrated 3-statement model, a Quality of Earnings review, and a banker-grade CIM. Normalizing earnings surfaced EBITDA 11% above management, and a curated M&A process drew 3+ LOIs in 90 days.

A sovereign clinical AI platform built where hosted AI APIs were legally off the table.

• 3 production AI products on one FHIR R4 engine, zero data egress
• 212 backend tests added and multiple security gaps closed

A top global venture firm made its scattered institutional memory queryable.

• Retrieval fell from weeks to seconds across years of firm memory
• 2 platforms unified 4 data sources; now trialed by the CEO

Lazer replaced a legacy Access system with an AI-native CRM.

• 100+ staff hours saved per month via automated document intake
• Hundreds of documents auto-ingested, parsed, and validated without human touch

Standardizing AI coding-agent deployment across a post-M&A codebase turned ad-hoc tool use into an org capability.

• PR review time down 20% across teams
• PR revert rate improved 65%, signaling higher quality

Lazer ran AI coding adoption as an engineering engagement, sequenced across 38 engineers.

• PR velocity up 40% within six weeks
• 13 external contractors offboarded, ~$300K/year saved per 10 engineers

A global consumer health company replaced ticket-based BI with self-serve agents.

• Kickoff to production in 21 days over three one-week phases
• 30+ brands in 60+ countries self-serve; ~20 workflows into one agent path

Lazer's discovery mapped a cross-functional packaging pipeline into a multi-agent design.

• Projected cycle: 16 weeks to 6 per SKU
• ~6,908 hours saved a year across 4,000 SKUs

A time-boxed pilot to prove document AI before a venture build.

• 80% accuracy on OCR-heavy insurance files where table-transformers failed
• Demo-ready prototype delivered in 6 weeks

Lazer shipped a governed natural-language BI agent plus an enterprise AI governance hub for a Fortune 500 CPG.

• 100% correct answers in the P&L pilot
• 4 systems integrated; Phase 2 approved

A high-volume support operation tested whether LLMs could safely automate first-line support.

• POC trained on 40,000+ real support tickets
• Validated by the CEO, CPO, and ML team, then grew into an embedded sprint

Enablement framed around existing consulting rigour, not novelty.

• ~300 staff across functions; 120 in hands-on in-person workshops
• Director Q&A built reusable Claude accelerators from messy inputs

Workflow-first training across the full reporting cycle, not AI theory.

• RTF/RISEN/RODES prompting for research, transcription and headlines
• NUJ-aligned guardrails kept AI inside editorial standards

Compliance treated as the unlock, not the blocker — Part 2 handled with PHI.

• De-identification layer strips PHI + 42 CFR Part 2 / SUD data
• Untouchable records became a sellable, monetizable data asset

Senior expertise turned into a self-serve asset instead of a standing interruption.

• Queryable knowledge brain lets juniors self-serve precedent
• Cut senior time ramping juniors ~60%

Scattered broker knowledge centralized before any outreach was retargeted.

• Auto-ingests every broker's email + text into one client brain
• Property-to-buyer matching lifted sales ~7%

Response speed made visible and enforceable before any sales process changed.

• Real-time tracking keeps every order under the 30-minute line
• Alerts before orders go cold recovered ~30% of leaking revenue

Site systems unified into one live source of truth before reporting was rebuilt.

• Cut monthly reporting from ~3 days to same-day (85% less)
• Query layer reclaimed 350+ analyst hours a year

Encoded the team's tribal order logic — like when stock actually ships vs. the date requested.

• Claude + Python normalize SAP orders into ERP format
• 15 hrs/week cut to under 2; forecast accuracy up 30%

Built data, AI workflows and evals as one system — every number carries a trace.

• Predictive models forecast; the LLM explains the reasoning
• $20M added to 10-year NPV; 6% lift in leasing value

Ran four compounding workstreams on a deployed eval platform, not a one-off scan.

• Discovery surfaced 75 shadow AI cases and 20 unauthorized MCP paths
• Consolidated AI sprawl to 12 sanctioned tools under an ISO 42001 policy

Treated accuracy like a CFO judges a forecast — graded on held-out data, not demos.

• Routed forecasts to a predictive engine, not the LLM
• Accuracy 60%→95%; NRR 82%→144%, live with 100+ enterprises

Replaced once-a-day static routes with continuous, AI-optimized re-routing on live data.

• Genetic-algorithm + RL engine re-routes around traffic and weather
• $18M fuel saved a year; utilization 64%→82%, delivery 22% faster

Replaced phone-only triage with a HIPAA-compliant telehealth platform wired into Epic and Cerner.

• NLP AI triage routes patients; 85% of visits went virtual
• 100k+ patients served, appointment waits cut 70%

Turned 10,000+ ignored plant sensors into 48–72h failure warnings with edge AI.

• Deep-learning prediction at 94% accuracy on critical equipment
• $12M saved a year; 15% less unplanned downtime across 12 plants

Replaced overnight batch inventory with a real-time, event-driven single source of truth.

• Sub-second stock updates across 500+ stores; 93% SKU forecast accuracy
• $15M recovered; a major contributor to 2x year-over-year sales

Replaced hours of manual 5G slice provisioning with a predictive, self-healing orchestrator.

• Slices created in under 60 seconds; 93% forecast accuracy
• 30% efficiency lift, 99.95% SLA compliance at 100M-subscriber scale

Replaced a sequential credit-risk process with parallel AI agents on a unified data mesh.

• Cut analysis from 3 days to 30 seconds
• $40M recovered; 80% less compliance overhead