We don't name customers. Their operations and their data are their business. Everything below is a real engagement described accurately, including where it currently stands. No inflated outcomes, no wall of logos. If a case study says "in proposal," that means they haven't signed yet, which is apparently a radical level of honesty for an AI company's website.
A structural steel fabricator quoting dozens of jobs a month, with estimating knowledge concentrated in a handful of senior people and job history locked inside QuickBase. Every quote was rebuilt from scratch. Margin outcomes were only understood after jobs closed, if at all.
A quoting and margin platform that syncs continuously from their QuickBase system of record, finds genuinely comparable historical jobs (matched on structural profile and labor density, not keywords), and puts material nesting optimization inline in the estimate builder. Every recommendation is backtested against jobs with known outcomes.
Estimators get a comparable set they can defend in a quote review. The platform reads with minimum-privilege access, never writes to the system of record, and runs in a fenced-off environment with database-level isolation.
Deployed and in production use. The pattern of sync, similarity, and margin analysis is the template for our quoting agents across fabrication and manufacturing.
A compliance software company whose customers upload internal procedures, manuals, and policies, thousands of pages, that need to be tracked against PHMSA pipeline regulations. Mapping them manually is slow, inconsistent, and doesn't scale.
An AI pipeline that ingests customer documents, generates a structured table of contents, and maps each section to the governing PHMSA regulations. Results are delivered back into the provider's own platform by API, without Junipix ever having direct access to their database.
Where the AI can't confidently link a section to a regulation, it doesn't guess. It flags the item into a human review queue. Accuracy over theater. This is our regulatory library working as a component inside someone else's product.
Under contract and in development. The same document-to-regulation mapping powers our policy gap analysis for operating companies.
A midstream operator whose commercial and regulatory teams work daily with FERC tariff filings. Dense, technical documents where the difference between two tariff provisions is real money, and finding the controlling language takes hours of expert time.
A domain-constrained tariff intelligence pilot: FERC filings and tariff records made searchable and answerable in plain English, with the exact provision cited, scoped to the operator's own systems and workflows.
General-purpose AI paraphrases tariffs from memory. This system answers only from the authoritative filings, cites the provision, and says so when the answer isn't in the documents.
Pilot proposal delivered; in active conversation.
A major union employer whose labor relations teams navigate collective bargaining agreements, company policies, and past practice across multiple workgroups. Answering "what does the contract require here?" means senior specialists searching hundreds of pages while the clock runs on grievances and operational decisions.
A domain-constrained intelligence system over the agreements and policies themselves: plain-English answers with the exact article and section cited, applicability explained by workgroup, and the strictest-answer logic applied where contract and policy overlap.
In labor relations, an uncited answer is unusable. Every response traces to contract language. It's the same approach we use for government regulations, applied to negotiated agreements.
In proposal. Enterprise engagements at this scale run on longer cycles, and we'd rather say that than imply otherwise.
A national distributor facing FDA FSMA 204 traceability requirements across thousands of SKUs, suppliers, and facilities. Add the everyday grind of freight negotiations, accessorial fees, and warehouse safety questions that each pull time from experts.
A family of agents on shared infrastructure: FSMA 204 applicability mapping at the SKU level, supplier readiness scorecards, an OSHA-plus-company-policy strictest-answer agent for warehouse supervisors, and logistics negotiation agents that benchmark lanes and flag fee leakage.
One regulatory backbone serving many workflows. It's the same pattern that lets an answer turn into an action: a notification drafted, a supplier flagged, an exception queued for human review with a full audit trail.
Proposed and in conversation. This engagement is where several catalog agents were first defined.
Every engagement above started the same way: a conversation about a specific operational problem, then a $2,500 audit that put numbers on it. The conversation part is free.