Outcome-priced, not hour-billed
We scope engagements against the number you're trying to move — cycle time, accuracy, deflection, working capital — and operate against it past go-live.
Agentic AI services and products doing the work that moves your business.
Most AI initiatives stop at the demo. Ours start there. We blend deep industry operators with senior engineers — and we engage on outcomes, not hours. The agents we ship hold up in production because they were built for production.
We scope engagements against the number you're trying to move — cycle time, accuracy, deflection, working capital — and operate against it past go-live.
Every agent ships with evals, guardrails, observability and a human-in-the-loop path. We don't release what we can't measure, audit or roll back.
Our teams operated supply chains, closed books and ran campaigns before they wrote agents. We pick the right model and pattern — your stack stays portable as the platforms evolve.
A defensible strategy your CFO can sign off on — in weeks, not quarters.
You name the metric. We ship the system, priced against the result.
Senior engineers on your roadmap. Billable from week one.
Pre-built. Production-grade. Tunable to your industry.
From signal to shelf — autonomous decisions across the network.
Close faster. Pay smarter. See cash before it moves.
Find the cohort. Ship the message. Stop the spend when it stops earning.
Three verticals. Practitioners who've operated in them. Agents tuned to the regulations, data structures and workflows that actually exist on the ground.
Compliance-aware agents for reconciliation, KYC investigation, claims adjudication, dispute handling and book-of-business analytics.
Agents for demand sensing, trade-promotion optimization, assortment planning, shelf intelligence and consumer engagement.
Agents for production planning, predictive quality, supplier resilience, field-service dispatch and aftermarket parts.
A snapshot of outcomes seen at enterprise scale across our three focus industries.
A Tier-1 retail bank reconciling across 14 source systems compressed quarterly close — and freed senior analysts for higher-value work.
A multinational consumer brand lifted forecast accuracy at SKU/store grain — and cut peak-week stockouts by a third.
A global automotive supplier reduced annual scrap by $12M after a predictive quality agent caught process drift before propagation.
“The honest test of an agentic AI program isn't how many demos it produces — it's how many decisions it owns at month-end. We build for the second one.” — TechnologyNow leadership perspective
TechnologyNow is headquartered in Dallas, with senior engineering pods across the United States, Latin America and Asia. We were started by operators who'd seen enough AI pilots quietly archived — and decided to build the firm that ships the production version.
Our principals have run global supply chains, closed quarterly books and led marketing organizations at Fortune 500 companies. That practitioner DNA shows up in the code.
Read our story →14114 Dallas Pkwy, Suite 600
Dallas, TX 75254
United States · Latin America · Asia
Time-zone aligned for the Americas and APAC.
Most first conversations take 45 minutes. By the end, you'll have a defensible read on whether agentic AI is the right answer for the problem in front of you — and what it would cost to find out.
Book a working session →A Tier-1 North American retail bank reconciling across 14 source systems.
Daily reconciliation across 14 source systems consumed the majority of mid-level analyst capacity. Quarterly close ran five business days — with the final two largely dedicated to break investigation. Headcount additions weren't an option; analyst attrition was.
An autonomous reconciliation agent took ownership of daily transaction matching, drafted candidate journal entries with supporting evidence, and investigated breaks — surfacing one-click approve-or-override packs the team could clear in seconds rather than hours.
The bank didn't replace its analysts — it redirected them. Where they'd been processing exceptions, they now investigate the patterns underneath them. The agent didn't take work away; it freed senior people to do senior work.
A multinational consumer brand running 60+ SKUs across 12,000 retail doors.
Forecast accuracy at SKU/store grain sat below 60%, which drove chronic out-of-stocks during promotional and seasonal weeks — and surplus inventory afterward. Trade investment was being spent on shelves that didn't have the product.
A demand sensing agent ingesting POS, channel sell-through, weather, promotion calendars and external demand signals revised short-horizon forecasts continuously — and triggered replenishment when reality diverged from plan. Every forecast revision came with a transparent rationale the planner could audit or override.
The agent didn't replace the demand planner. It gave them a forecast that updated as fast as demand did — and a reason for every revision the category manager could defend.
A global automotive supplier producing safety-critical components across 11 plants.
Process drift was only detected at the inspection station — by which point thousands of parts had already been produced. Annual scrap exceeded $20M, and quality engineering was spending more time on forensics than prevention.
A predictive quality agent monitoring sensor telemetry, line speeds, ambient conditions and material lot data surfaced drift signals four to eight hours before they reached the inspection station — with recommended corrective parameters for the line supervisor to confirm.
The agent didn't replace the quality team. It turned a reactive function into a preventive one — and let senior engineers spend their time on the patterns, not the casualties.