Selected Work
AI solutions delivered in production.
Every project below represents a live system — not a pilot, not a proof of concept, not a slide deck. Real businesses, real workflows, real outcomes.
Professional Services — Legal · 2024
Contract Review & Drafting Copilot
67%
Reduction in review time
3x
Contract throughput increase
94%
Attorney satisfaction with AI drafts
Challenge
A mid-sized law firms in-house team was spending an average of 6 hours per contract review cycle — reading, cross-referencing precedents, and generating first-draft redlines. With an increasing volume of commercial agreements, the team was at capacity without a viable path to scale.
Solution
Tesseraz built a contract intelligence copilot integrated directly into the firms existing document management workflow. The system identifies key clauses, surfaces relevant precedents from past agreements, flags non-standard terms, and generates a structured first-draft redline — all within the attorneys review environment.
Outcome
The firm reduced average contract review time from 6 hours to under 2 hours per agreement. Attorneys reported higher confidence in their reviews and a significant reduction in post-execution issues traced back to missed clause variations.
Logistics & Supply Chain · 2024
Exception Management Automation Platform
72%
Autonomous exception resolution
85%
Reduction in resolution time
$1.2M
Annual cost avoidance
Challenge
Novas operations team was managing over 400 shipment exceptions per week — manually triaging, investigating, communicating with carriers, and updating their TMS. Two full-time coordinators spent the majority of their day on exception management, with no capacity for proactive network optimization.
Solution
Tesseraz designed an agentic exception management platform that monitors the shipment feed in real time, classifies exception types, triggers appropriate resolution workflows, communicates with carriers via API and email, and routes only genuinely complex or high-value exceptions to human coordinators — with full context pre-assembled.
Outcome
The platform now autonomously resolves 72% of exceptions end-to-end. The two coordinators who previously managed exception volume now focus on carrier relationships and network optimization. Average exception resolution time dropped from 4.2 hours to 38 minutes.
Financial Services · 2023
Advisor Intelligence & Client Briefing System
45 min
Saved per meeting per advisor
60
Advisors on the platform
+22%
Client meeting satisfaction score
Challenge
Beacons advisors were spending 45–60 minutes before each client meeting pulling together account data, portfolio performance, previous interaction history, and relevant market context. The process was time-consuming, inconsistent, and often incomplete — leading to meetings that felt reactive rather than prepared.
Solution
Tesseraz built a client intelligence system that automatically generates a structured briefing document before each scheduled meeting. The system retrieves account data, portfolio positions, recent transactions, CRM notes, and macro-relevant context, then synthesizes it into a consistent one-page brief tailored to the meeting agenda.
Outcome
Advisors recover an average of 45 minutes per client meeting in preparation time. Meeting quality scores from client surveys improved significantly. The briefing system has become the standard pre-meeting workflow for all 60 advisors across three offices.
Healthcare · 2024
Revenue Cycle Intelligence System
38%
Reduction in denial rate
11 days
Reduction in A/R days
$3.8M
Annual revenue recovered
Challenge
Stratas revenue cycle team was managing a denial rate of 12% — significantly above industry benchmarks. Their existing process for identifying denial patterns was manual: analysts pulled reports monthly and made recommendations that took weeks to implement. By the time issues were addressed, millions in claims had already been affected.
Solution
Tesseraz built a revenue cycle intelligence platform that ingests claims data in near-real time, identifies denial patterns by payer, code, and submission pathway, generates root cause hypotheses, and alerts the revenue cycle team with recommended adjustments — before the next submission cycle.
Outcome
Within two quarters, Strata reduced their denial rate from 12% to 7.4%. The revenue cycle team shifted from monthly retrospective analysis to weekly proactive management. Days in accounts receivable decreased by 11 days.
Retail & E-commerce · 2023
Unified Customer Support AI
68%
Autonomous contact resolution
<90s
First response time
4.4 / 5
Customer satisfaction score
Challenge
Crest Commerces customer support team was handling 8,000 contacts per month across chat, email, and their portal. Over 65% of those contacts were categorized as tier-1 — order status, return initiation, and product questions that required no specialized judgment. Support agents were frustrated, and first-response time had grown to over 6 hours.
Solution
Tesseraz deployed a support AI system across all three channels — chat, email, and portal — trained on Crests product catalogue, order management system, return policy, and historical support resolutions. The system handles tier-1 autonomously with full order context, routes tier-2 cases to agents with pre-assembled summaries, and escalates tier-3 with full conversation history.
Outcome
The AI now handles 68% of all contacts autonomously. Human agents focus entirely on complex and relationship-sensitive cases. First-response time for automated contacts dropped to under 90 seconds. Customer satisfaction scores improved from 3.6 to 4.4 out of 5.
Manufacturing · 2024
Predictive Maintenance & Quality Intelligence
63%
Reduction in unplanned downtime
56%
Reduction in defect rate
$2.1M
Annual scrap and downtime savings
Challenge
Vantages flagship facility was experiencing an unplanned downtime rate of 8.4% — above their target threshold and causing cascading production delays. Simultaneously, a quality defect rate of 3.2% in their primary product line was producing significant scrap and rework cost. Both problems were being managed reactively.
Solution
Tesseraz built a predictive intelligence platform that ingests sensor data from 140 machines and process parameters from their MES. The system detects degradation patterns that precede equipment failure and identifies process conditions that correlate with quality defects — alerting maintenance and quality teams in time to intervene.
Outcome
Unplanned downtime decreased from 8.4% to 3.1% within the first six months. Quality defect rates in the monitored product line dropped from 3.2% to 1.4%. The maintenance team shifted from calendar-based to condition-based maintenance scheduling.
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