Software that thinks the way your business does.

A regional payments startup needed to process cross-border B2B transactions with multi-currency settlement, without building six separate banking integrations in-house.

Legacy banking APIs were slow and inconsistent across countries, and KYC/AML requirements varied by jurisdiction — creating a real risk of approval delays and compliance gaps at launch.

We built a unified gateway abstraction layer normalizing six banking APIs behind one interface, with automated KYC/AML checks built into onboarding. Transaction approval time dropped from days to minutes.

A small-business lender wanted a real-time financial health view for loan applicants, instead of reviewing static PDF statements manually.

Applicant financial data lived across disconnected spreadsheets and accounting tools, making consistent risk assessment slow and error-prone across underwriters.

We built a data ingestion pipeline connecting to common accounting platforms, auto-generating a standardized risk dashboard for underwriters. Underwriting time dropped by 60%.

A neobank wanted to launch a digital wallet with virtual card issuance for retail customers, layered on top of its existing core banking system.

Card issuance needed to be PCI-DSS compliant and integrate with a live core banking system — with zero downtime during rollout to existing customers.

We delivered a wallet app with tokenized card issuance, rolled out in phases behind feature flags, achieving a zero-downtime migration from the legacy core.

A regional clinic network needed a telehealth booking and video-consultation platform to reduce dependency on phone-based scheduling.

Patients and staff were juggling phone bookings and three separate scheduling tools, causing frequent double-bookings and a high no-show rate.

We unified booking, reminders, and video consults into one platform with smart, timed reminders — cutting no-shows sharply and reducing double-bookings to near zero.

A health insurer wanted to digitize its paper-based claims intake and adjuster review process.

Manual claims review took ten-plus days on average, with a high error rate caused by re-keying data from scanned forms.

We built an OCR-assisted intake pipeline feeding a structured adjuster review dashboard, cutting average claims processing time to under three days.

A homecare provider needed to monitor chronic-care patients remotely between in-person visits.

Wearable device data arrived in inconsistent formats from different device brands, and clinicians had no single view of patient trends over time.

We built a device-agnostic ingestion layer and a clinician dashboard with trend alerts, enabling earlier intervention for at-risk patients.

A boutique hotel group wanted to reduce reliance on third-party OTAs by launching a direct booking engine.

Room inventory and pricing lived in a disconnected property management system that synced with a 15-minute lag, causing periodic overbookings.

We built a real-time inventory sync layer and a direct booking flow, eliminating overbookings and increasing direct bookings by 35%.

A travel agency wanted a self-service itinerary builder for independent travelers assembling their own trips.

Combining flights, hotels, and local experiences from multiple supplier APIs into one coherent itinerary was a manual, spreadsheet-driven process for agents.

We built a modular itinerary builder pulling live data from multiple supplier APIs, cutting itinerary creation time from hours to minutes.

A vacation-rental operator managing 200+ properties needed centralized, real-time availability across multiple booking channels.

Manual calendar updates across four channels caused frequent double-bookings, forcing costly last-minute guest cancellations.

We built a channel-manager integration syncing availability every 60 seconds across all listing platforms, eliminating double-bookings entirely.

A K-12 school network wanted a learning management system that improved student engagement, not just content delivery.

The existing LMS had high content drop-off and gave teachers no visibility into exactly where students disengaged in a course.

We built engagement analytics with drop-off tracking and gamified milestones for students, raising course-completion rates by 40%.

An enterprise L&D team needed to track compliance-training completion across roughly 5,000 employees.

Training records were split across spreadsheets and a legacy LMS, with no automated compliance reporting ahead of audits.

We built a unified training platform with automated compliance reporting and manager dashboards, cutting audit-prep time from weeks to hours.

A university wanted to connect students with peer tutors on demand, rather than through manual email requests.

Manual matching via email led to slow response times and inconsistent tutor availability, frustrating students who needed help before deadlines.

We built a real-time matching and scheduling marketplace with in-app messaging, cutting average match time from days to under an hour.

A home-services startup wanted an on-demand marketplace connecting customers with vetted local providers.

Manual provider dispatch caused long customer wait times and gave the company no consistent way to track service quality.

We built an automated dispatch engine with provider ratings and live tracking, cutting average dispatch time to under five minutes.

A staffing platform wanted a two-sided marketplace matching freelance crews with short-term gigs.

Payment escrow and dispute resolution needed to feel instant and trustworthy to both crews and hiring businesses, or trust in the platform would erode fast.

We built an escrow-based payment flow with milestone releases and an in-app dispute workflow, increasing repeat bookings by 50%.

A regional grocery chain wanted a delivery marketplace connecting shoppers with independent drivers.

Order-to-delivery time was inconsistent, and drivers had no optimized routing, leading to unpredictable delivery windows for customers.

We built route optimization and live order tracking into the driver and customer apps, reducing average delivery time by 25%.

A growing retailer needed real-time inventory visibility across four warehouses feeding one online storefront.

Manual stock reconciliation between warehouses caused overselling during peak sales events, leading to canceled orders and refunds.

We built a real-time inventory sync system with automatic stock holds at checkout, eliminating overselling during flash sales.

An apparel brand needed a storefront that could handle roughly 10x traffic spikes during major sale events.

The existing storefront had crashed during previous sale events, directly costing revenue and damaging customer trust.

We rebuilt the storefront on a scalable headless architecture with CDN caching, handling a Black-Friday-scale spike without downtime.

A retailer wanted to reduce support ticket volume by giving customers a self-service returns and exchange portal.

Return requests were handled manually by support staff, creating a multi-day turnaround and a steadily growing ticket backlog.

We built a self-service returns and exchange portal with automated shipping-label generation, cutting return-related tickets by 70%.

A SaaS company wanted to predict which customers were at risk of canceling, before renewal conversations started.

Churn signals were scattered across product usage logs, billing history, and support tickets, with no single model tying them together.

We built a churn-prediction model combining usage, billing, and support signals, giving customer success teams a 30-day early warning on at-risk accounts.

A payments company needed to flag fraudulent transactions in real time, without adding friction to legitimate checkouts.

The existing rule-based fraud checks had a high false-positive rate, blocking real customers almost as often as it caught fraud.

We built a real-time anomaly-detection model that cut false positives by 45% while catching more true fraud than the rules-based system it replaced.

An e-commerce retailer wanted personalized product recommendations based on real browsing behavior, not fixed merchandising rules.

Generic "customers also bought" logic wasn't converting, and the catalog was too large for manual curation to scale.

We built a recommendation engine using collaborative filtering and visual similarity matching, lifting average order value by 18%.

An insurer wanted to automatically extract structured data from scanned claim documents instead of manual data entry.

Documents arrived in inconsistent formats and handwriting styles, which broke simple off-the-shelf OCR pipelines.

We built a document-intelligence pipeline combining OCR with a trained extraction model, reaching 94% field-level accuracy on real claim documents.

A growing startup wanted to reduce support response times without scaling up headcount at the same rate.

Support agents were manually re-answering the same common questions across hundreds of tickets a day, with no assisted drafting.

We built an AI-assisted triage and response-drafting tool trained on the company's own help docs, cutting first-response time by 65%.

A multi-store retailer needed better demand forecasting to cut down on both overstock and stockouts.

Forecasts were based on simple historical averages that ignored seasonality, promotions, and local trends store by store.

We built a forecasting model incorporating seasonality, promotions, and local events, cutting overstock costs by 22% within two quarters.

A call center wanted incoming calls automatically routed based on customer intent, rather than static IVR menus.

Manual IVR-based routing frustrated customers and misrouted roughly a third of all incoming calls to the wrong team.

We built a speech-to-intent classification model integrated directly into the call flow, cutting misrouted calls by 60%.

A manufacturer wanted automated visual quality inspection on its production line, replacing manual spot-checks.

Manual inspection was inconsistent between shifts and inspectors, and simply couldn't keep pace with production speed.

We built a computer-vision defect-detection model deployed directly at the line, catching defects at full production speed with consistent accuracy.