Nine deployment patterns from the best-documented areas of enterprise AI. Each card shows the problem, what we build for it and what comparable deployments achieved. Every effect figure is an industry benchmark, not our client result.
REAL ESTATE · SALES
AI LEAD AGENT FOR BROKERAGES
PAIN //
One inquiry hits five agencies at once, and the first answer often wins. Night and weekend leads are dead by morning.
WE BUILD //
An agent answering portal and WhatsApp inquiries in seconds, 24/7. Qualifies budget, area and timeline, books the viewing, hands the buyer to CRM with full context.
EXPECTED EFFECT //
- Leads contacted within an hour are about 7x likelier to qualify than leads reached later (Harvard Business Review)
- Roughly a third to half of deals go to the first responder
CUSTOMER SUPPORT
SUPPORT AGENT ON YOUR KNOWLEDGE BASE
PAIN //
Support headcount grows with ticket volume. Routine questions bury the queue; response times slide.
WE BUILD //
An agent grounded in your docs that closes routine tickets 24/7 and routes the rest to the right team with full context attached.
EXPECTED EFFECT //
- 71–82% ticket deflection in documented deployments (Stanford Digital Economy Lab, 2026)
- 90–95% automation at scale in documented deployments (Stanford Digital Economy Lab, 2026)
- ~71% median productivity gain under escalation-based oversight
KNOWLEDGE OPS
COMPANY KNOWLEDGE ASSISTANT
PAIN //
Policies, contracts and project history live in scattered folders and in people’s heads. Staff keep asking colleagues the same questions, settled decisions get reopened, and onboarding drags.
WE BUILD //
An internal assistant over your documents — answers with source links, permissions-aware, wired to the systems where the files already live. Runs inside your perimeter when the data demands it.
EXPECTED EFFECT //
- Cross-repository data gathering cut from 40+ hours to under one in a documented deployment (Stanford Digital Economy Lab, 2026)
- Faster onboarding: new hires look answers up instead of asking around
DOCUMENT OPS
INBOUND DOCUMENT PROCESSING
PAIN //
Invoices, contracts and KYC packs arrive in any format. Teams re-key the data by hand, and errors creep in.
WE BUILD //
Extraction, completeness checks and posting into your systems with no manual entry — humans handle exceptions only.
EXPECTED EFFECT //
- A documented invoice deployment ran on 2 FTE instead of 7 and returned >$1M, live in 8 weeks (Stanford Digital Economy Lab, 2026)
- 60–70% cycle-time reduction typical for document workflows
SALES INTELLIGENCE
100% CALL & MESSAGE REVIEW
PAIN //
Managers sample a few percent of calls. Deal-loss reasons stay anecdotal; coaching lags by weeks.
WE BUILD //
AI review of every call and thread: QA scoring, loss-reason patterns, per-deal follow-up recommendations on a live dashboard.
EXPECTED EFFECT //
- Review coverage: from ~2–5% sampled to 100% of interactions
- Call review became a real-time coaching and QA source in documented deployments (Stanford Digital Economy Lab, 2026)
ANALYTICS & FORECASTING
REAL-TIME BI + AI FORECASTS
PAIN //
Problems surface in the monthly report — weeks after the money is gone. CRM, accounting and ops never share one screen.
WE BUILD //
A live dashboard across CRM, accounting and operational systems with AI-annotated anomalies and demand forecasts.
EXPECTED EFFECT //
- Forecast-driven operations in a documented retail deployment: waste −40%, stockouts −80%, EBITDA margin doubled (Stanford Digital Economy Lab, 2026)
- Anomalies flagged same-day instead of at month close
HR & RECRUITING
AI CANDIDATE SCREENING
PAIN //
Recruiters drown in applications; screening eats hours per role while strong candidates accept offers elsewhere.
WE BUILD //
AI screening with bias-checked evaluation, automated first-round flows and a feedback loop from hiring outcomes back to the criteria.
EXPECTED EFFECT //
- Screening time per role: 3 hours → 3 minutes in a documented deployment (Stanford Digital Economy Lab, 2026)
- +83% intake efficiency, +75% candidate conversion
- The same deployment was built in about a month
MARKETING
AI CONTENT PRODUCTION AT SCALE
PAIN //
Campaigns crawl through agency workflows for weeks; personalization stays at segment level because content can’t keep up.
WE BUILD //
Brand-safe AI content generation with a human review layer — AI produces, your team refines the final 20%.
EXPECTED EFFECT //
- Campaign time-to-market: 7 weeks → 6 hours in a documented deployment (Stanford Digital Economy Lab, 2026)
- 2x click-through rate; >80% cut in production time
- Zero-error tolerance kept via human sign-off
SECURITY OPS
AI ALERT TRIAGE
PAIN //
Alert floods bury small security teams; only high-priority items get investigated, the rest age out unseen.
WE BUILD //
AI triage that classifies alerts and filters out false positives in seconds, escalating only the judgment calls to your analysts.
EXPECTED EFFECT //
- Throughput 1,500 → 40,000 alerts/month in a documented deployment (Stanford Digital Economy Lab, 2026)
- Coverage of all alerts on 1.5 FTE instead of 6 — no layoffs, team redeployed to threat hunting
- Escalation-only workload for humans
