Platforms we build
Real systems, told as use cases. Every entry below is a system we have built or are equipped to build today. Client names, product names, and identifying details are always changed for confidentiality.
Every system below is real work we ship. Some are deployed for specific clients (anonymized). Some we built and run ourselves. Others are patterns we deliver on demand. Client names, product names, and identifying details are always changed for confidentiality.Real systems we ship. Names and identifying details changed for confidentiality.
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Dozens of reps, hundreds of accounts, and territory rules in a spreadsheet
The Situation
A national distributor's reps fought over accounts across a wide regional territory. Boundaries lived in a spreadsheet no one updated. Managers had no view of field activity. New reps took months to ramp because the institutional knowledge was in three people's heads.
What We Built
- Interactive territory map with priority-based clustering
- AI lead scoring from 0 to 100, refreshed by activity feedback
- Natural-language search across the account base
- AI chat assistant with full territory context
- Auto-generated account briefings before every visit
- Activity logging that feeds back into the scoring loop
The Outcome
Equipment dealers were buying ad clicks from leads they would never close
The Situation
Industrial equipment dealers were spending heavily on lead-gen agencies that delivered raw form-fills. Most leads were not real buyers. Sales reps wasted weeks chasing tire-kickers while the actual prospects went to competitors.
What We Built
- AI-native marketing and knowledge platform, one tenant per dealer
- Lead-quality scoring on every inbound before it hits the sales team
- AI-assisted content tied to each dealer's machine catalog
- Self-hosted on shared infrastructure with single-admin auth per tenant
- Every closed deal feeds back into the scoring model
The Outcome
Reps walked into calls knowing nothing about the prospect because nobody has 30 minutes to research before every meeting
The Situation
A B2B sales team running 8 to 12 calls per day per rep was supposed to research each prospect company before every conversation. In practice this happened maybe half the time. The other half, reps showed up cold and burned the first 10 minutes asking questions a quick LinkedIn check would have answered. Win rates suffered.
What We Built
- AI agent that researches a prospect company end-to-end before every scheduled call
- One-page brief generated automatically: org structure, recent news, hiring signals, tech stack clues, recent funding
- Talking points specific to the prospect, tied to the rep's product positioning
- Common-ground signals (shared connections, mutual customers, similar deployments) surfaced for opener material
- After each call, the rep marks what was useful, and the next brief gets better
- Calendar integration so the brief is ready 30 minutes before the call without anyone asking
The Outcome
Marketing handed sales hundreds of leads a month. Half got one email, then nothing.
The Situation
A growing B2B company had marketing generating qualified leads and sales focused on the hottest ones. Everything in between (lukewarm leads, not-ready-yet leads, ghosted-but-not-dead leads) fell into a black hole. Marketing called it a sales problem. Sales called it a marketing problem. Real revenue leaked between the two.
What We Built
- Autonomous nurture agent that owns the lead between marketing handoff and sales-ready
- Decides what to send next per lead based on behavior signals and stated interest
- Generates content (emails, content recommendations, scheduled calls) per lead, not per cohort
- Triggers a sales handoff the moment a lead signals real intent
- Every won and lost deal feeds back into the model's decisions about what works for whom
- Visible to both marketing and sales so neither team has to wonder what's happening to their leads
The Outcome
"We always discount 15%" was the default, even when the customer was about to sign anyway
The Situation
A complex industrial seller priced every quote by gut feel. The default move on big deals was a 15% discount, applied reflexively, regardless of whether the customer needed it to close. Sales managers approved the discounts because nobody had the data to argue. Margin leaked on every deal that didn't actually need the discount.
What We Built
- AI pricing recommendation per quote based on deal context, customer history, win/loss history of similar deals, and current capacity
- Suggested price with a "why" explanation the rep can show the customer
- Discount-justification workflow that requires explicit reasoning, not reflex
- Every won and lost deal teaches the model which prices actually closed
- Margin trail per deal so the CFO sees where discounts compound and where they don't
- Plays with the existing CRM and quoting tool, doesn't replace them
The Outcome
Three days at a trade show, hundreds of business cards collected, and most of them sat in a drawer until the next quarter
The Situation
An industrial company invested heavily in trade-show presence: booth space, travel, staff time. The team came home with a stack of business cards and lead-capture forms. By the time someone in the office got around to entering them and following up, the leads had cooled. Many never got a call at all. ROI on trade-show spend was unmeasurable.
What We Built
- Mobile-first lead capture at the booth: barcode scan, quick form, voice notes
- Real-time AI scoring on each captured lead, based on the conversation notes and a company-data lookup
- Hot leads routed to specific reps before the show ends, with a draft follow-up email ready to send
- Cooler leads enter a nurture sequence automatically
- Post-show dashboard with conversion tracking back to closed revenue
- Every deal that closes from a show lead feeds back into the next show's scoring
The Outcome
The manufacturer sold through dozens of distributors and had no idea which ones were actually growing the brand
The Situation
A manufacturer that went to market through a distributor network knew aggregate sales numbers but had no visibility into which distributors were investing in the brand, which were coasting on existing customers, and which were quietly losing share. Quarterly business reviews were show-and-tell instead of evidence-based.
What We Built
- Distributor performance dashboard pulling sales, marketing activity, customer feedback, and territory signals into a single view your team can ask questions of
- Per-distributor health score with the underlying signals visible
- Targeted enablement content generated for each distributor based on what they need
- Underperformance early-warning so the channel manager intervenes before quarterly review
- Every intervention's effect feeds back so the model improves over time
- Distributors get their own view of their own data, with benchmarking against anonymized peers
The Outcome
We built our own project management because nothing on the market reads our work for us
The Situation
We needed a project management system that an AI could read end-to-end. Linear and Jira track tickets, but the context lives outside the issue, scattered across chat, docs, and pull requests. The kind of AI-readable organization the YC AI-native playbook describes did not exist for our shape of team, so we built one.
What We Built
- Self-hosted PM tool with a native MCP server
- AI agents read project state, list issues, and propose plans without human translation
- Every commit, comment, and state change recorded so the system can learn from it
- Runs on the same pattern we sell: agents propose, humans approve, the system learns from what happens next
- Used internally to plan every engagement on this page
The Outcome
Seven agents, graduated autonomy, marginal cost zero
The Situation
A product team in a regulated space needed a marketing engine that could plan, draft, post, and measure without a marketing team. The brief: spend on agent tokens, not on a five-person content shop. Every claim had to verify against live source data before going public.
What We Built
- Seven specialized agents covering research, drafting, scheduling, publishing, monitoring, attribution, and review
- Graduated autonomy: each agent earns more decision-rights as its track record proves out
- Verification step against live source data before anything ships
- Separated from the product's own infrastructure so it can't corrupt the data plane
- Marginal cost of operation: $0 per month beyond token spend
The Outcome
Production stopped at the worst time, and the maintenance team only knew the machine was struggling after it broke
The Situation
A mid-market manufacturer running multiple production lines tracked equipment health through a combination of operator notes, work-order history, and a few sensor readings logged into a legacy CMMS. Failures were obvious in hindsight but invisible going forward. Maintenance ran reactively. Every breakdown cost a shift of production and an emergency overtime call.
What We Built
- Ingestion pipeline pulling sensor readings, operator notes, and work-order history into one layer the AI can search and reason over
- Pattern-detection models trained on each line's specific failure history
- Risk score per machine, refreshed continuously, with a "why" explanation the maintenance lead can trust
- Auto-drafted preventive work orders the maintenance lead approves in one click
- Every actual failure (or successful prevention) feeds back so the model improves
- Integration with the existing CMMS so it stays the system of record
The Outcome
A defect made it through five inspection points and into a customer's hands. Nobody could explain how.
The Situation
A precision manufacturer ran multiple inspection points across the production line, each staffed by a human inspector. Catch rates varied by inspector, by shift, by fatigue level. Subtle defects (especially aesthetic or pattern-based ones) slipped through. When a customer returned a defective unit, the trace-back through five inspection points was slow and inconclusive.
What We Built
- Camera systems at key inspection points with on-device AI vision for defect detection
- Operator console that flags suspect units, shows the AI's reasoning, and lets the human accept or override
- Training pipeline where every override (false positive or false negative) feeds back into the model
- Per-defect-type detection so the system handles the dozens of failure modes specific to this product
- Trace-back: every unit's full inspection history searchable from a single record
- Integration with the existing QMS so quality data stays where the auditors expect it
The Outcome
The sales-ops analyst spent three days a week pulling reports nobody read in time to act on
The Situation
A growing B2B company had a sales-ops function buried in commission calculations, pipeline reports, forecast updates, territory adjustments, and ad-hoc requests from the CRO. The work was important but most of it ended up in spreadsheets that landed too late to change any decision. The analyst was burnt out and the CRO still didn't feel they had a clean view of the business.
What We Built
- Autonomous commission calculator pulling from CRM, billing, and HR
- Continuously updating pipeline forecast with "why" explanations the CRO can trust
- Auto-drafted weekly business-review deck: humans approve, AI generates
- Plain-language query interface so the CRO can ask "which deals shifted out of this quarter and why" and get an answer
- Every quarter's actuals teach the model, so forecast accuracy improves over time
- Integration with whatever CRM, billing, and HR stack is already in place
The Outcome
Purchasing was a stack of emails, an aging ERP screen, and someone's gut feeling about which supplier to use this week
The Situation
A manufacturer's purchasing team handled hundreds of RFQs and POs a week through a combination of email, an aging ERP, and tribal knowledge. Comparison shopping happened informally. Anomalies (a supplier raising prices, a delivery slipping) got noticed by the purchaser if they happened to notice. The ERP had the data but nobody had time to query it.
What We Built
- AI agent that reads incoming RFQs and matches them against current supplier inventory, pricing, and lead times
- Drafted POs ready for the purchaser to approve, with the reasoning visible
- Anomaly detection on supplier pricing, lead times, and quality signals
- Plain-language query interface over the full purchasing history
- Every actual delivery (on-time, late, quality issue) feeds the supplier model
- Sits inside the existing ERP environment, no rip-and-replace
The Outcome
A customer churned and the customer-success manager said "I never saw that coming"
The Situation
A subscription business with hundreds of active customers had a customer-success team running on a mix of NPS surveys, occasional check-ins, and gut feel about which accounts were at risk. The signals that predicted churn (declining usage, support escalations, executive sponsor turnover) were all there in the data, but nobody had time to look at them across the whole book.
What We Built
- Continuous churn-risk score per account, refreshed in real time from usage, support, and account-team input
- "Why" explanation per score so the CSM trusts it
- Recommended save-action per at-risk account, drafted by an agent, approved by the CSM
- Automated check-in cadence calibrated to each customer's actual signals, not a generic schedule
- Every saved (or lost) account teaches the model
- Executive view so the CRO and CFO see the book's risk profile
The Outcome
Hundreds of thousands of technical documents, and the answer to "which spec applies to this serial number" lived in one engineer's head
The Situation
A large industrial OEM with a multi-decade product history had accumulated hundreds of thousands of technical documents across product lines: original equipment manuals, service bulletins, engineering change notices, parts catalogs, application notes, internal training material. Some were native PDFs, some were scans of typewriter-era originals. The institutional knowledge of which document applied to which serial number, configuration, or field condition lived in the heads of a small number of senior engineers and field-service leads.
What We Built
- Hybrid search combining full-text and semantic embeddings across the full document library
- OCR pipeline for scanned legacy documents, with structure-aware chunking that respects section and table boundaries
- AI answers with page-level citations back to the source document and a one-click link to the exact page
- Permission-aware access so internal-only content stays internal and external documents can be queried by dealers and field techs
- Serial-number-aware retrieval that filters to documents matching a specific configuration
- When a technician marks an answer wrong, the next retrieval improves
The Outcome
"Which part fits this old machine" was the question that lived in one technician's head. They were retiring.
The Situation
A distributor of industrial spare parts dealt with thousands of customers running equipment from dozens of OEMs across multiple decades of product. When a customer called for a replacement, finding the right SKU meant cross-referencing OEM part numbers, aftermarket alternatives, supersession history, and known compatibility quirks. The senior parts manager held most of this in memory. When they were on vacation, response times tripled.
What We Built
- AI-native parts catalog that matches OEM part numbers across manufacturers and aftermarket equivalents
- Compatibility graph mapping which parts fit which machine configurations and serial-number ranges
- Demand forecasting per SKU per region to inform reorder timing
- Customer-facing search where the buyer can paste an OEM part number or describe the application and get the right SKU back
- AI assistant for the parts desk that surfaces all the institutional quirks ("this generation of pump has a non-standard thread")
- Every fulfilled order updates the compatibility graph
The Outcome
The senior engineer was retiring in six months, and most of what they knew had never been written down
The Situation
A specialty manufacturer relied on a handful of senior engineers and technicians who had been with the company for decades. Their expertise covered process tolerances, supplier quirks, failure modes, and the "this is how we actually do it" knowledge that never made it into formal documentation. The company knew this was a risk but had no good way to capture it. Asking someone to "write everything down" produced bullet lists that lost all the context.
What We Built
- Structured interview protocol with senior staff, captured as video and audio
- Transcription and AI summarization that preserves the speaker's voice and reasoning
- Photo and document capture during interviews so visual references are linked to the explanation
- AI knowledge layer that lets future engineers ask questions and get answers in the senior engineer's actual words, with the original recording linked
- Cross-referencing with existing documentation so the captured knowledge augments (not replaces) the formal SOPs
- Permission-aware so sensitive process knowledge stays internal
The Outcome
The dealer agreement was signed three years ago, and nobody remembered the exclusivity clause until the lawsuit landed
The Situation
An industrial distributor managed hundreds of supplier agreements, dealer agreements, customer contracts, and NDAs. Each one had obligations and renewal dates that nobody systematically tracked. Important clauses (exclusivity, audit rights, change-of-control) were invisible until they were violated.
What We Built
- AI reading every incoming and existing contract, extracting key terms into a structured model
- Comparison against the company's playbook with flags on non-standard clauses
- Auto-drafted redlines for negotiation, with reasoning the lawyer can review
- Obligation tracking with calendar reminders for renewal dates, audit windows, exclusivity expirations
- Plain-language search over the contract base ("which of our supplier agreements have audit-rights clauses?")
- Every negotiated outcome teaches the playbook
The Outcome
The auditor asked "why did we approve that spec deviation in 2024?" and three people spent a week reconstructing the answer
The Situation
A regulated manufacturer (ISO-certified, with periodic external audits) had to demonstrate that every decision, deviation, and corrective action followed the documented quality system. The actual paper trail was distributed across emails, signed paper forms, an aging QMS, and the memories of the quality manager. Audit prep was a multi-week fire drill every time.
What We Built
- Every action in the existing operational systems generates a structured audit artifact
- Plain-language query over the full historical audit trail
- AI search that surfaces relevant past decisions, deviations, and corrective actions for any auditor question
- Pre-built audit reports generated on demand instead of constructed from scratch
- Sign-off workflows that are AI-aware so approvals leave the right trail by default
- Permission-aware access for internal staff and external auditors
The Outcome
The CEO had ten dashboards open, and none of them answered the question they actually had
The Situation
A growing company's operational stack had accreted over years: a CRM, a billing tool, a docs platform, a project tracker, a chat system, a spreadsheet for the things nothing else handled. Every tool was someone's source of truth for something. To answer any cross-cutting question, the CEO had to email three department heads and wait two days. The data was technically all there, but no one could actually ask it questions.
What We Built
- Unified data layer that ingests from every operational system the company uses
- Single canonical schema that maps accounts, contacts, deals, projects, tasks, decisions, and finances into one graph the AI can read
- AI agents that read across the whole graph with permission scoping
- Plain-language query interface for the C-suite and operators
- Operational front-ends (CRM-like, PM-like, finance-like surfaces) that read and write the unified layer
- Every operational decision becomes something the agents learn from
- Self-hosted on infrastructure the company owns
The Outcome
IT wouldn't sign off on replacing the CRM, the ERP, or the project tracker. The company still needed AI that could see everything.
The Situation
A mid-market company had years of commitment, training, and integration sunk into its existing SaaS stack. Ripping any of it out was a non-starter politically and financially. But adding generic AI assistants to each tool individually solved nothing. Those bolt-ons could only see the one tool they were inside.
What We Built
- Read-and-write integrations with every system in the stack
- Unified model the AI can read that doesn't replace any system of record, but creates a single layer AI can actually reason over
- AI agents that span the whole stack: read from CRM and finance, propose action in PM and chat
- Write-back to the systems of record so the existing tools stay the source of truth for their domain
- Plain-language query interface that returns answers grounded in real data with citations to source systems
- Permission-aware: respects the access control of every underlying tool
The Outcome
ERP knew the inventory, MES knew the production schedule, CRM knew the customers, and nobody knew everything
The Situation
A vertically integrated industrial company ran a typical operational stack: ERP for inventory and finance, MES for the production floor, CRM for the sales pipeline, separate systems for field service and parts. Every cross-system workflow (quote-to-cash, RFQ-to-fulfillment, service-to-billing) required humans to copy data between four screens. Mistakes were inevitable. Reporting was always weeks late.
What We Built
- Vertical-specific data layer connecting ERP, MES, CRM, field service, and parts into one graph the AI can read
- AI agents that own the cross-system workflows end-to-end: an RFQ comes in, the agent pulls capacity from MES, prices from CRM history, drafts the quote, routes for human approval
- Field-service AI that sees the customer's full history (sales, parts, prior service) before the tech rolls
- Operations dashboard that surfaces cross-cutting issues (a production delay's impact on customer commitments) immediately
- Every workflow outcome feeds back into the agents' decisions
- Self-hosted because industrial data doesn't leave the company
The Outcome
Five businesses, five separate stacks, and "which company needs my attention this week" was an unanswerable question
The Situation
A founder or operator running multiple businesses (a holding company, a venture studio, a serial founder's portfolio) had each business on its own stack, often with different vendors. Aggregating across the portfolio meant a monthly call where each business presented its own slides. Pattern detection across the portfolio (where is growth strongest, where is cash tightest, where is the team under-resourced) was effectively impossible.
What We Built
- Multi-tenant data layer where each business has its own isolated data plane
- Aggregation layer that surfaces portfolio-level insights without violating tenant isolation
- AI agents per tenant for the day-to-day, plus a portfolio-level agent for the operator
- Plain-language query interface for both levels: each business's CEO queries their own data, the operator queries across all of them
- Cross-business pattern detection that learns from every signal (e.g., a successful campaign in one business gets surfaced as a candidate for the others)
- Self-hosted with strong tenant isolation because each business has its own customer data
The Outcome
The ERP was implemented years ago, the consultants who knew it are gone, and replacing it would take three years and break everything
The Situation
A mid-market company depended on an aging ERP. The system worked, mostly. But the UI was a relic. New workflows took months to add. Customizing reports required a specialist nobody could hire anymore. The system handled what it was built for when it was installed and nothing new. Replacing it was a three-year project nobody wanted to greenlight.
What We Built
- AI-native layer alongside the legacy ERP, not replacing it
- Read-and-write integration: ERP stays the system of record for transactional data
- Modern front-end surfaces for the workflows that need them (sales, customer service, ops dashboards)
- Plain-language query over the ERP's data so anyone can ask questions without learning the legacy UI
- AI agents that handle the new workflows the ERP was never built for
- Incremental delivery: ship a new workflow every few weeks instead of a three-year rebuild
The Outcome
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