What the work looks like.
The work below is real. We've anonymized and generalized the details to protect confidentiality, and no client, agency, program, or product is named. Each pattern is set out the way it actually happened: the problem, what we found, what we did, what changed, and why it matters, including the parts that are still in progress.
Infrastructure and water
Fragmented public data, no operating picture
The problem: A market ran on public data, regulatory filings, bond disclosures, procurement records, and nobody had assembled any of it into a usable form.
What we found: The information existed, but it lived across dozens of agencies and formats with no shared schema, and nobody had built the normalization layer underneath it.
What we did: We built a data pipeline that ingests, validates, and normalizes the sources, then applies deterministic scoring to surface the signal that mattered.
What changed: The system is in production, running ongoing data operations with staged validation and provenance tracking, and it serves both a data backend and a customer-facing product.
Why it matters: Most fragmented-data problems look unsolvable because of the scale rather than the difficulty, and the right ingestion and normalization architecture is what makes them tractable.
National security and government markets
A contractor drowning in opportunities it cannot evaluate
The problem: A government contractor needed to filter thousands of solicitations down to the handful worth pursuing, and had no systematic way to do it.
What we found: Opportunity data was split across nine separate federal sources, each with a different schema, update cadence, and reliability.
What we did: We built a scoring engine that reconciles the sources and ranks opportunities against the contractor's actual capability and win history, feeding a structured proposal-writing workflow.
What changed: The platform is in mature production use, running a nine-stage pipeline from opportunity discovery through a compliance-checked proposal export.
Why it matters: The bottleneck in competitive procurement is rarely writing ability. It's knowing which opportunities are worth writing for in the first place.
Healthcare
A regulated industry running on five disconnected systems
The problem: Medical aesthetics practices were operating across five to seven separate software systems for records, payments, scheduling, and communication, each vendor-locked and none built for the clinical workflow.
What we found: No existing platform was purpose-built for aesthetic-medicine work, so practices were adapting general medical or wellness tools instead, and the gaps showed up exactly where safety mattered most.
What we did: We designed a consolidated, multi-tenant platform with database-level tenant isolation and a built-in contraindication-checking engine for treatment safety.
What changed: The architecture, compliance framework, and data model are fully specified and documented. The full application has not yet been built.
Why it matters: If you're consolidating a fragmented regulated workflow, you have to get the compliance and data-isolation architecture right before writing a single feature.
Technology and consumer
A hard-to-reach audience with no engaging practice tool
The problem: Parents of children with speech sound disorders had no accessible, engaging way to reinforce speech therapy at home between sessions.
What we found: Existing apps were either too young and unfocused or too clinical to hold a child's attention, and none of them were built around actual speech-therapy progression.
What we did: We built a gamified practice app aligned to standard speech-therapy progression, designed under strict children's-privacy constraints with no data collection by default.
What changed: The MVP is complete, functional, and runs on device across the target platforms. Production usage and outcome data are not yet available.
Why it matters: When you build for children, the compliance constraint has to shape the product from day one rather than get layered on afterwards.
National security and government markets
Automation that cannot touch the open internet
The problem: A cleared organization wanted AI-driven workflow automation, but every existing framework assumed cloud connectivity that a classified environment can't allow.
What we found: No commercial or open-source orchestration platform was built to run entirely inside a closed, self-hosted perimeter with a mandatory human-approval step before any sensitive action.
What we did: We built a workflow-orchestration platform from the ground up for that constraint, with classification-aware state tracking and a signed human-approval gate at every sensitive step.
What changed: An early released version exists with a strong automated test suite and full architecture documentation. Hardware-signer integration and a live, end-to-end deployment remain open work.
Why it matters: Some environments can't adopt the standard version of a tool category at all, so the work is building the version that respects the constraint instead of asking the client to waive it.
Operations and market development
A decision that has to survive later scrutiny
The problem: Decision-makers evaluating a large investment, acquisition, or capital-intensive facility build needed to keep evidence, calculation, and assumption clearly separated, and to produce a record that would hold up under later review.
What we found: The usual approach of spreadsheets, email threads, and unstructured document repositories blurs sourced fact and inference together, and leaves no clean audit trail.
What we did: We designed a structured diligence framework that enforces citation traceability and separates fact from calculation from assumption, alongside a companion cost-modeling engine for capital-intensive builds that returns ranged estimates instead of a single guess.
What changed: The diligence framework exists as a documented design with no code committed. The cost-modeling engine is built, tested, and calibrated with backtesting.
Why it matters: When a decision carries real legal and financial consequences, how defensible the record is matters as much as the conclusion itself.
The range is the point
The environments above don't share a market. A federal capture manager, a facility engineer, a medical practice, and a parent looking for a speech-therapy app have nothing in common except the shape of the problem underneath: fragmented information, a hard constraint, and a decision that has to hold up later. That repetition, rather than any one case, is the real evidence that the method transfers. Across these patterns, Eleven 186 has shipped 8+ first-of-their-kind systems, with viable builds typically live within 120 days.
Tell us the problem
If your situation looks structurally like one of these, or like none of them, tell us what's going on.