We build the systems behind products people trust with their health, their wallets, and their work.

The Challenge

A person's health record doesn't live in one place. Lab results sit in a portal PDF. Wearable data lives on a phone. Past prescriptions are scattered across pharmacy apps. Hospital visits live in EMRs they can't read. The result: consumers have more health data than ever and less clarity than ever.

The existing options force a bad choice. Self-tracking apps are shallow — step counts and water reminders, not biomarkers. Clinical EHRs are deep but unreadable. Generic AI chatbots will happily hallucinate a diagnosis. None of them give a thoughtful, longitudinal, personal view that a consumer can actually trust.

Zovio's founders wanted to fix that — a single place where a person could see their health unfold over time, understand what was changing, and walk into a doctor's office prepared.

The Approach

We built Zovio as a clinically-grounded consumer product, not a wellness toy. That meant solving three hard problems in parallel:

1. 01
   Ingestion that actually works in the real world

   Lab PDFs come in dozens of formats, with values reported in different units, against different reference ranges, by different labs. We had to normalize all of that without losing precision.
2. 02
   AI that knows what it doesn't know

   A health product cannot afford a confident hallucination. We grounded every AI-generated insight in source data with traceable citations, and built an evaluation harness that gates model output against clinical sanity rules before it ever reaches the user.
3. 03
   A consumer-grade experience over enterprise-grade plumbing

   Privacy posture, encryption, and access controls had to meet healthcare expectations while the surface remained as approachable as a notes app.

Inside the Solution

• Multi-source ingestion — lab report PDFs (OCR + structured parsing), wearable APIs, and manual entry.
• Biomarker normalization — unit conversion and reference-range mapping so a value from one lab is comparable to a value from another.
• Unified timeline — every biomarker, vital, and event on a single longitudinal view; tap a point to see the source document.
• Narrative summaries — AI-generated, plain-English readouts of what changed since last time, grounded in the underlying numbers.
• Risk & trend surfacing — early signal on values trending out of range, with clear, non-alarmist framing.
• Doctor-share mode — a clean, single-page export for in-person visits.
• Privacy by default — data lives encrypted, the user owns export and deletion, and no data is used to train external models.

Outcomes

Tech & Architecture

A React Native client over a Python/FastAPI backend on AWS, with PostgreSQL for structured records and a vector store for retrieval-grounded summarization. LLM output passes through a clinical-rules evaluator before it's surfaced. Data at rest is encrypted; access is logged and auditable.

The Challenge

Cannabis legalization moved faster than cannabis literacy. A first-time or returning customer walks into a dispensary and sees a wall of jars labeled with strain names that mean almost nothing: the same name across two shops can have completely different chemistry and completely different effects. Budtenders try to help, but training varies, turnover is high, and a five-minute conversation can't substitute for genuine personalization.

The downstream effect is brutal for everyone: customers buy by guess, have inconsistent experiences, and don't return. Dispensaries see flat basket sizes and burn staff time on basic education. Brands can't tell which products are actually a fit for which kind of customer.

Cannabee's founders wanted to replace the wall-of-jars problem with a personalized, chemistry-grounded recommendation — built mobile-first and respectful of how regulated the category is.

The Approach

We built Cannabee on a single conviction: stop recommending strains, start recommending outcomes. Strain names are unreliable; the underlying cannabinoid and terpene profile is what actually determines effect. So we modeled the world that way.

1. 01
   Goal-first onboarding

   A short conversational flow captures what the user is trying to achieve — sleep, focus, social, relief — plus their experience level, sensitivity, and preferred formats. Under 60 seconds, no medical jargon.
2. 02
   A product graph grounded in chemistry, not labels

   SKU-level cannabinoid and terpene data, normalized across partner dispensaries.
3. 03
   A hybrid recommender

   Content-based matching on chemistry and goal, layered with collaborative signal as the user base grew.
4. 04
   Explanations, not black boxes

   Every recommendation comes with a plain-English "why we picked this" so the user learns over time and trust compounds.
5. 05
   Regulated by design

   21+ age gating, geofenced availability, and compliance flows woven into the product — not bolted on.

Inside the Solution

• Sub-60-second onboarding — that captures the inputs a recommender actually needs.
• AI recommender — ranking products by goal-fit, chemistry profile, and (over time) feedback signal.
• In-app education — short, contextual explainers that surface only when relevant, so a curious user can learn without being lectured.
• Dispensary partner dashboard — consumer preference insights and basket signals, with no PII leaking to brands.
• Closed-loop feedback — thumbs up/down and structured post-purchase prompts that re-train the model.
• Compliance posture — age verification, regional availability, and a careful split between marketing and recommendation surfaces.

Outcomes

Tech & Architecture

A React Native app for iOS and Android over a Python backend, with PostgreSQL for structured product and user data and embeddings for content-based matching. The recommender is a hybrid model — content + collaborative — with an LLM strictly scoped to explaining recommendations, never generating product claims.

The Challenge

The legal profession is drowning in repetitive work it shouldn't be doing. Contract reviews. DPA redlines. Vendor NDAs. Bulk diligence. Legal research that takes a senior associate half a day to compile. The industry's first wave of "AI assistants" gave lawyers slightly faster autocomplete, not actual leverage — and most of them were unusable for enterprise legal teams because client data couldn't leave the premises and couldn't be used to train models.

Jurisynk's founders wanted to skip the assistant layer entirely and build what the next decade of legal work would actually look like: AI agents that execute, with the security posture a general counsel will sign off on.

The Approach

We built Jurisynk as an agent-native workspace, not a chat app with a sidebar. The architecture started from three principles:

1. 01
   Agents do work, they don't suggest work

   Drafting, redlining, research, intake — each is owned by a specialized agent that completes a multi-step task end-to-end, with a human in the loop on outcomes rather than every keystroke.
2. 02
   Playbooks before models

   A firm's drafting standards, redline preferences, and risk tolerance live in machine-readable playbooks. The model executes against the playbook; the playbook is the source of truth.
3. 03
   Enterprise security is the product, not an afterthought

   On-premise deployment, role-based access, audit trails, and a no-training contract for client data — built in from day one, not retrofitted.

Inside the Solution

• Drafting agent — generates contracts and clauses from playbooks, flagging risk and deviation as it writes.
• Review & redline agent — runs DPA, vendor, and NDA reviews at scale, surfacing deviations from a firm's standard.
• Legal research agent — case law and regulatory research with grounded citation, not generative guesswork.
• Email / intake agent — triages incoming legal requests from the inbox, routes them into the workspace, and starts the right workflow.
• Collaborative canvas — real-time co-editing, tracked changes, and a full audit trail across the team.
• Document management — every artifact, version, and decision in one searchable workspace.
• Workflow controls — dashboards, approval gates, and access controls so a head of legal can run the function, not just use the tool.
• Integrations & admin — API surface and admin controls for embedding Jurisynk into a firm's existing stack.
• Security & compliance — end-to-end encryption, SSO (SAML / OIDC), RBAC, audit logs, on-prem option, GDPR / DPDPA / ISO 27001-aligned, SOC 2 Type II in progress.
• Evaluation framework — every agent has an offline eval harness that gates model changes before they reach production.
• No-training assurance — client data is contractually excluded from any model training, under stated terms.

Outcomes

Tech & Architecture

A Next.js + TypeScript front-end with real-time collaborative editing over a Python / FastAPI backend. A multi-agent orchestration layer coordinates specialized agents for drafting, review, research, and intake, each grounded in retrieval over a firm's own corpora. Security is built in: SSO, RBAC, end-to-end encryption, audit trails, and an on-premise deployment option for firms that require full data sovereignty.