Machine learning · 2026
InterfaceGenie
Retrieval-grounded document and deck generation.
Generated documents fail in a specific way: they are fluent and unsourced. The platform is built around the opposite constraint — retrieval first, generation second, with the corpus as the authority and the model as the writer. Three product modes share the pipeline: a corporate reporting wedge, a lighter student and creator mode, and a professional planning mode for designers.
- Status
- Active
- Domain
- Applied LLM · Document systems
- Role
- Architecture, RAG pipeline, product
- Year
- 2026
Stack
- RAG
- Model Context Protocol
- Vector search
- Postgres
- Stripe
- TypeScript
A platform that generates corporate reports and presentation decks from a governed corpus, with a retrieval pipeline, tool integration through the Model Context Protocol, and a planning mode for designers.
Retrieval as the authority
The generation stage is deliberately constrained.
Content is retrieved and ranked from a governed corpus before any prose exists, and the model composes within that evidence rather than around it. The architecture pack that documents this treats the retrieval pipeline, the data model and the cost analysis as first-class design artifacts — because in a generation product the retrieval quality and the unit economics are the product.
Tooling through an open protocol
External capability is wired through the Model Context Protocol rather than bespoke integrations, so a new data source becomes a server rather than a fork of the application.
It is a slower first integration and a dramatically cheaper tenth.
Staged from local to cloud
The build is explicitly sequenced: a fully local stage that proves the pipeline on a single machine, then a cloud stage that adds multi-tenancy, billing and scale.
The order means the expensive infrastructure decisions are made against a working system rather than a guess.
Three modes, one pipeline, different failure costs
The corporate wedge, the creator mode and the professional planning mode share retrieval and generation but not their tolerance for error.
A student deck that misattributes a statistic is embarrassing; a corporate report that does the same is a liability. The modes therefore differ in how much of the pipeline is allowed to be automatic — the corporate path keeps a human verification surface over every retrieved claim, and the lighter modes trade that for speed knowingly rather than by omission.
Cost per artifact is a design input
A generation product where each document costs an unbounded amount of inference does not have a business model, it has a bill.
Token accounting is treated as an architectural constraint from the start: retrieval depth, context assembly and model selection are all tuned against a target cost per artifact, and the analysis lives beside the architecture documents rather than being discovered at scale.
Security as a documented workstream
Payment handling, secret management and history hygiene are tracked as explicit artifacts alongside the feature work rather than as a pre-launch checklist.
For a product taking payment and holding client documents, treating the security review as a deliverable — with its own specification, its own findings and its own remediation record — is the difference between a claim and a position you can defend.
Pipeline
Retrieval before generation
Stage Can reject
-
01
Corpus
Governed sources
-
02
Chunk + embed
Vector index
-
03
Retrieve
Ranked evidence
-
04
Compose
Draft
Model writes within the evidence
-
05
Bind citations
Sourced claims
-
06
Render
Document or deck
Skills exercised
What the build
actually demanded.
Against the corpus · 15 systems
This system Corpus median
Retrieval
- Chunking and embedding strategy for long-form source documents
- Hybrid ranking over a governed corpus
- Citation binding between generated claims and sources
- Evaluation of retrieval quality independent of generation
Product
- Multi-mode product scoping from one pipeline
- Subscription billing and entitlement design
- Designer-facing planning and production workflows
- Cost modelling per generated artifact
Platform
- Model Context Protocol server integration
- Staged local-then-cloud build sequencing
- Relational data model for documents and revisions
- Security review and secret-handling remediation
What it establishes
-
Three product modes
Corporate · creator · professional planning
-
Protocol-native tooling
New sources are servers, not forks
-
Local stage first
Infrastructure chosen against a working pipeline
-
Cost per artifact budgeted
Token accounting is an architectural constraint
-
Security as a deliverable
Specified, reviewed and remediated on the record