Products: The portfolio

// PORTFOLIO

Client-funded research, productized.

Most of our products did not start as products. They started as a client problem we were paid to solve: a film studio drowning in script coverage, a training firm with no map of its workforce's skills, a triage line that needed to scale without losing clinical rigor. We solve the specific problem, ship it, and watch what happens next.

When the same shape of problem shows up a third time, we stop rebuilding it by hand. We extract the durable core (the analysis engine, the evaluation harness, the voice pipeline) into something multi-tenant, configurable, and sellable on its own. The client engagement funds the R&D; the product captures the recurring value.

NOTE: SOME NAMES PROVISIONAL // CAPABILITIES ARE REAL

FIG. AGENCY → PRODUCT FLYWHEEL

REF: PROD-00 Agency-to-product flywheel: problem, solution, pattern, product

01 // Live: paying customers

REF: PROD-01

ProofRoom

An AI platform for analysing scripts and long documents. Run structured analysis modules, refine conversationally, and teach the system your house methodology so its judgment becomes yours.

The hard part: versioned, reproducible analysis pinned to a hash of its inputs, and a golden-corpus evaluation harness so quality is measurable, not a vibe.

STATUS: LIVE // FLAGSHIP // DAILY USE

LiveView productarrow_forward

REF: PROD-02

Content Agents

An autonomous content fleet: multi-agent pipelines that research, write, illustrate, and publish to a live CMS on a cadence, with semantic deduplication so they don't repeat themselves.

The hard part: agent-to-agent fact-checking and embedding-based dedupe that let the fleet publish directly to a live CMS, unattended, without drifting off-brand.

STATUS: LIVE // RUNNING ON CADENCE

LiveView productarrow_forward

02 // In build

REF: PROD-03

VoiceReady

A speaking-practice app that analyses how you actually sound, not just what you said: filler words, vocal tremor, pacing, hedging.

The hard part: real acoustic analysis via a Praat phonetics sidecar, and best-in-class voice vendors behind swappable interfaces.

STATUS: IN BUILD // MOST AMBITIOUS

In buildView productarrow_forward

REF: PROD-04

SkillsNav

A workforce-intelligence platform that interviews your people by AI voice, builds a skills inventory, finds the gaps, and tells you what to train and who to hire.

The hard part: turning a fluid voice interview into normalized, comparable skills data, and grounding recommendations in the customer's own course catalogue.

STATUS: IN BUILD // MVP

In buildView productarrow_forward

REF: PROD-05

Semantic Signal

Analytics for the post-search era: measure whether large language models cite your brand, how, and why.

The hard part: turning 'do LLMs mention us?' into ~14 stable, comparable, formulaic metrics across non-deterministic models.

STATUS: IN BUILD // EMERGING CATEGORY

In buildView productarrow_forward

REF: PROD-06

Conversational Forms

Turns a long, intimidating form into a natural conversation. Fill it by talking or chatting, with a drag-and-drop builder behind it for whoever designs the form.

The hard part: keeping a free-flowing conversation and a partially-filled structured form in sync, bidirectionally, generated from the form definition rather than hard-coded.

STATUS: IN BUILD // GENERALIZING FROM CLIENT WORK

In buildView productarrow_forward

03 // Prototypes: concept proven, parked for market

REF: PROD-07

CrisisSim

AI-driven crisis-simulation training: a live, branching scenario run by an AI crisis-master that puts a leadership team through a realistic incident.

The hard part: stateful, multi-turn scenario orchestration that escalates believably instead of railroading participants.

STATUS: PROTOTYPE // DEMONSTRATED

REF: PROD-08

TIA

A clinical triage assistant: a real-time voice front end talking to a patient, backed by a supervisor LLM that makes the triage decision against an established clinical protocol.

The hard part: separating a fast conversational voice layer from a slower, authoritative supervisor that owns the decision and the record.

STATUS: PROTOTYPE // REGULATORY CAUTION

REF: PROD-09

Meeting Intelligence

RAG over meeting transcripts with cited answers. Ask what was decided and get the passage that proves it. (Name provisional.)

The hard part: faithful retrieval with citations a reader can verify, over messy, unstructured transcript data.

STATUS: PROTOTYPE

REF: PROD-10

FDA Intelligence

Regulatory-document semantic search and RAG over large, unstructured regulatory corpora.

The hard part: faithful retrieval over long regulatory documents where a wrong or unsourced answer is worse than no answer.

STATUS: PROTOTYPE

REF: PROD-11

SnapCrave

Photo-to-nutrition food AI: point a camera at a meal and get a structured nutrition estimate.

The hard part: turning a single vision input into a defensible structured estimate rather than a confident guess.

STATUS: PROTOTYPE

REF: PROD-12

Vector Field

Multi-model business-intelligence orchestration: route a question across models and reconcile the answers.

The hard part: deciding when an ensemble actually beats the best single model, and proving it.

STATUS: PARKED // SPEC / MOCK

04 // Integration tooling: MCP connectors

Alongside the product lines we ship and maintain a small set of Model Context Protocol (MCP) connectors that let AI assistants read and operate enterprise systems directly. They are plumbing, not headline products. But they are why several lines above can reach into a customer's existing systems without bespoke integration work each time.

SAP HANA MCP connector: puts an LLM directly on a HANA analytics warehouse (stdio + HTTP). Open-source on npm.
Odoo MCP connector: read and operate Odoo ERP over XML-RPC; underpins CRM, billing, and content-publishing automations.

FIG. MATURITY PIPELINE

REF: PROD-PIPE Product maturity pipeline: prototype to build to live

How we build Start a conversationarrow_forward

05 // The flywheel

A client problem, paid for once. A product, paid for again.

Every product on this page traces the same four-stage loop. We do not start with a roadmap and look for buyers; we start with a paying client and a problem worth solving exactly.

Problem: a client pays us to solve one specific, hard thing inside their business.
Solved: we ship it into production, narrowly and well, and watch it run.
Recurs: the same shape of problem arrives a second and a third time, from a different industry.
Productized: we extract the durable core into something multi-tenant and configurable; the engagement funded the R&D, the product captures the recurring value.

LOOP: PROBLEM → SOLVED → RECURS → PRODUCTIZED

FIG. PROBLEM → PRODUCT FLYWHEEL

REF: PROD-FLY Agency-to-product flywheel: a client problem is solved, recurs across industries, then is extracted into a product

06 // Integration tooling: MCP connectors

Connectors that let the products reach into enterprise systems.

FIG. MCP CONNECTOR LAYER

REF: PROD-MCP Provider-abstraction layer: a single MCP interface fronting swappable enterprise database and ERP back ends

MCP-first is a method, not an afterthought. We build the integration once, as a Model Context Protocol connector behind a clean, swappable interface. Any product or agent above can then read and operate a customer's existing systems without bespoke wiring each time. The same abstraction that lets us swap a model also lets us swap the system of record.

REF: MCP-DB

Enterprise database connector

Puts an LLM directly on an enterprise analytics warehouse over both stdio and HTTP transports, so an assistant can query governed business data in place, under the warehouse's own access controls.

Open source

REF: MCP-ERP

ERP connector

Reads and operates an open-source ERP over its native RPC layer. It is the plumbing under several CRM, billing, and content-publishing automations, so agents act on the real records rather than a copy.

Open source

PATTERN: ONE INTERFACE // SWAPPABLE BACK ENDS