Problem

A mid-market consultancy was losing a full FTE to weekly client reporting — data pulled from five systems, reconciled in spreadsheets, formatted by hand.

Approach

We architected a lightweight agentic pipeline that ingests source data, validates it against business rules, and assembles client-ready briefs through a reviewed LLM layer. Built on a serverless Next.js + Python edge stack.

Outcome

40 hours of manual work per week reduced to under 90 minutes of review. Reporting accuracy improved measurably, and the freed analyst capacity moved to revenue-generating client strategy.

Problem

A Series B SaaS was losing competitive deals because its dashboard felt static next to AI-forward alternatives. Rewriting the stack felt too risky; bolting on a chatbot felt too shallow.

Approach

We introduced a structured reasoning layer over the existing data model — natural-language query, proactive insights, and action suggestions — without disturbing the underlying product. Delivered as a decoupled Next.js surface wired to the legacy API.

Outcome

Won three enterprise deals previously lost on product perception. Time-to-insight for existing users dropped from minutes to seconds, and the company now positions the product as AI-native without having rebuilt it.

Problem

A global DTC brand was running three fragmented CMSs across web, mobile, and retail kiosks. Every new market launch cost weeks of engineering; AI personalization was impossible on the legacy stack.

Approach

We designed a headless, structured content architecture with typed schemas and an AI-ready metadata layer. Content is authored once, rendered across every surface, and exposed cleanly to recommendation and generation models.

Outcome

New-market launch time dropped from six weeks to under one. Personalization experiments that were previously infeasible now ship in a sprint. The platform became a competitive asset rather than a cost center.

Problem

A regional hospital network was losing critical decision-making time to manual chart review. Clinicians spent hours each week extracting structured information from free-text notes — work that was error-prone, inconsistent, and impossible to audit at scale.

Approach

We built a HIPAA-aligned document intelligence pipeline using a fine-tuned extraction model and a retrieval-augmented generation layer. Clinical notes are processed on ingestion, key fields extracted and validated against the patient record, and outputs surfaced in the existing EHR interface with full provenance trails.

Outcome

Chart review time reduced by over 70% per patient. Coding accuracy improved, reducing claim rejections. The system now processes thousands of notes per day with a human-in-the-loop review step that takes minutes, not hours.

Problem

A mid-sized edtech platform had rich curriculum content but no way to personalize it. Students moved through material at a fixed pace regardless of mastery, and instructors had no visibility into where comprehension broke down.

Approach

We designed an adaptive content engine that tracks mastery signals per concept, routes students to targeted practice or accelerated material, and surfaces a weekly insight report for instructors. Built on a structured curriculum graph with an LLM layer for natural-language explanations and hint generation — all grounded in approved course material.

Outcome

Average assessment scores improved by 18% within the first term. Instructor intervention time shifted from reactive to proactive. Course completion rates increased, and the platform secured two new district contracts citing the AI layer as a differentiating feature.