Selected engagements, portable patterns.

Problem

Every integration starts as its own program. The second borrows from the first by copying it, the third borrows from the second, and within a year there are 5 codebases that are 80% the same and differ in ways nobody wrote down. Fixing a retry bug means finding it 5 times. Onboarding a different platform means a rewrite, because that platform's field names are threaded through logic that has nothing to do with fields. The tell is visible from outside. A practice whose work all names the same CRM either chose that specialism, or built itself into it and cannot say which. Portability is the claim every integration shop makes and the one nobody can demonstrate, because demonstrating it means having built the same thing twice and being able to show what stayed the same.

Architectural Solution

Separate what a system means from how a platform says it, then never let the two touch. Handlers speak workforce vocabulary only, employees, sales, appointments, and they are the only surface a caller may call. Interpreters translate that vocabulary into one platform's wire conventions and back. Configuration carries the pointers, in 2 layers, a platform config shared by every client and a client overlay carrying only what differs for one tenant. A tenant name appears in a filename and in a value. It never appears in a code path. Credentials never appear in a file at all, they are named in the overlay and resolved from the environment at startup. The consequence is that replacing a source CRM is 1 handler module plus 1 platform config, and onboarding a new client on a platform already supported is 1 overlay file with no library work at all.

Tangible Result

The separation holds at 2 scales, and the second one is the proof of the first. At the platform scale, a CRM swap is 1 interpreter plus 1 config and a new tenant on a platform already supported is 1 overlay file with no library change at all. At the runtime scale, the specification was built again on .NET, in C# and F#, and on Node. Both carry the identical doctrine, prose for prose. The 272 lines that changed between the reference and the .NET build are every one of them a code sample, and the sections that moved are exactly the sections that carry code. Nothing in the design had to be renegotiated to reach a second runtime, which is the strongest available evidence that the layer boundaries are real rather than described. The Node build then earned 1 module the other 2 do not have, for answering requests from strangers, and the specification says outright that it is Node only and why. Most of it identical everywhere, and the differences named rather than smoothed over.

Problem

The agency was paying for advertising with no instrument connecting any of it to a sale. 1 phone number served all 355 links on the site, so the source of every call was erased the moment somebody picked up. Underneath that sat the real constraint. The client held an editor login. Somebody else held the repository, the deployment, and the content dataset. They could reword a heading. They could not add a route, add a table, read a log, or export their own content in any structured form. The rebuild was forced by an access problem, not an engineering one.

Architectural Solution

Recover the content, then rebuild underneath it. 176 pages and 59 images were archived first, and 155 education articles were extracted into flat files the client now controls, where a git log shows every change to any of them. The build is Next.js App Router on Node with PostgreSQL, no ORM and no content management system (CMS). Content is code, so regulated copy carries a review diff on every change rather than being editable by anybody optimising a layout. Schema came before application code. Migration 001 builds visitors, sessions, call clicks, leads, consents, sync state and an audit log, with first touch on the visitor and last touch on the session, both stored. The cookie names, the reserved module paths and the timezone pin were all fixed before a single event was collected. Purchased leads land in the same table as website leads, authenticated per vendor with the source tag taken from the key rather than the payload, so cost per acquisition compares directly across both.

Tangible Result

The agency owns the repository, the database, the deployment, and the record. Every lead is written before it is forwarded and carries a tracking id minted in code rather than by the insert, so it survives a slow vendor and a failed write. Consent is stored verbatim as it was displayed. The published 24 month retention window has a purge script behind it, the published 30 day privacy deadline is written at insert rather than computed on read, and the accessibility statement is generated from an automated audit across 17 pages. The archive pass also surfaced a published relay number that reached nothing, on 528 pages, 1 wrong digit and the highest value fix in the engagement. The chain is instrumented from the lead forward. The call leg is what remains.

Problem

Your system of record holds the truth. Appointments, callbacks, the schedule, everything downstream depends on it. But your team does not live inside the system of record during the working day. They live in their calendar. Whatever the system of record knows that the calendar does not is invisible to the people doing the work. They either keep 2 windows open and remember to cross check, or they let things slip. When something slips the trust degrades, the renewal probability falls, and the case dies quietly. The obvious fix is each person hand copying entries onto their own calendar, which produces duplicates, drift, and forgotten records. Originally deployed against a dialer centric insurance CRM. The same gap appears anywhere a calendar and a system of record live in separate places.

Architectural Solution

A continuous one way sync that keeps the system of record authoritative and projects its truth onto the team's calendar within minutes. Every record runs a 2 layer dedupe pass, an exact marker lookup for anything the bot wrote before, then a fuzzy pass across a configurable window that adopts events somebody typed in by hand and patches them in place rather than creating a second copy. State is visual rather than absent, a completed entry prefixes with [Done] and mutes to graphite instead of vanishing, so the day still reads as a day. The pipeline trace is written as JSON Lines with dimensional fields precomputed, so it lands in Tableau, Power BI, Looker Studio or Excel with no intermediate data engineering between the integration and the report.

Tangible Result

The team opens their calendar, the day is laid out. Each event title carries the client name and disposition, each location carries the dialable phone number, each description carries the notes left in the system of record the last time anyone touched the lead. Completed entries visually cross out in place rather than vanishing. Adopted manual entries stop generating duplicates after the first cycle. The compliance officer can reconstruct any individual event's lifecycle from the audit log. The principal can show a prospect or a renewal client a live dashboard of throughput against the trace stream, no manual data reconstruction required. The integration is not just a sync. It produces its own evidence of value as it operates, in a format the client's BI tools consume directly.

Problem

Existing clients calling about something important should never wait while you triage cold inbound. But most practices' voicemail surface doesn't know the difference. Every voicemail lands in the same generic inbox, indistinguishable, and sits there unread for hours or days until somebody manually triages. The worst case is the one that compounds, a missed call from someone who's already paying you, calling about something that needs an answer today, rolling into a generic no context summary the next morning. The relationship erodes. The renewal slips. Sometimes the case goes with it. Customer facing teams need live visibility into every voicemail AND a way to instantly know which callers are known to the practice versus cold. Originally deployed at an insurance agency on Asterisk PBX, Gmail, and Slack. The pattern applies anywhere a practice has to distinguish 'someone we know' from 'someone we don't' the moment a call lands.

Architectural Solution

A polling pipeline that reaps PBX generated voicemail emails from Gmail (OAuth scoped read only), extracts the caller's phone number, cross references the book of record to find the matching lead, then queries status to determine priority. Known client callers trigger live Slack alerts with the actual voicemail audio auto uploaded as a threaded reply that plays inline in the channel. No inbox diving, no link clicking, no app switching required. Unknown callers are silenced during business hours and rolled into a single morning digest the next day. When a caller leaves multiple voicemails between cycles, the pipeline groups them by normalized phone and posts 1 consolidated alert with every audio threaded chronologically underneath, rather than N separate alerts the team has to mentally reassemble.

Tangible Result

35 days of production data, 907 voicemails captured, 97.9% delivered with audio attached as a threaded Slack reply, median 2.7 seconds from PBX drop to channel playable (95th percentile under 5). 26.8% received an explicit team reaction inside Slack, a measurable floor on how often inbound got actively triaged. In the old workflow, that visibility was 0 and surfacing time was measured in hours. The team now responds to active client voicemails during the same call window the customer is still expecting an answer, not the next morning. The self healing backfill pass recovers the 2.1% of upload failures automatically. The v3 rebuild moved the system from a 7,045 line single file script to roughly 80 files across handler/interpreter/config layers, behind ~360 automated checks, with cutover from old to new completed in roughly 5 seconds via the 1 click installer. Live in production today serving a CS team across multiple shifts. Full instrumented breakdown published in the Field Notes blog post.

Problem

The moment your practice scales past 1 practitioner, the inbound has to find the right one. Not just anyone, the right one. By specialty. By license. By state. By availability. By whatever constraints actually govern your routing. Without an enforcement layer, it lives in shoulder taps and tribal knowledge. Some people claim work they aren't actually available for. Others are available but invisible. The rules get applied inconsistently. Stale requests clutter the working channel for weeks after the work has already closed. The team needs real time visibility into who's free, automatic enforcement of the rules that actually govern routing, and a way to keep the working channel readable as volume scales. Originally deployed inside a customer service floor at an insurance enrollment center on Slack. The same engine applies to any multi practitioner practice distributing live inbound, medical groups routing patients to specialists, law firms distributing matters to associates, brokerages routing leads by license and state.

Architectural Solution

A real time routing engine that runs inside the team's messenger channel. The bot watches for inbound requests, parses the relevant attributes from the message, then routes to a live tracked pool of available practitioners. It pulls only those whose latest status is Ready, declared via Block Kit availability polls posted directly inside the channel. Rule aware pool selection applies the firm's routing rule. Fallback to the secondary pool fires automatically when the primary has nobody ready. A nightly cleanup pass removes stale request messages and their entire reply thread, with full audit log, rate limit aware retry, and a dry run mode for safe rollout. The architecture is the first production deployment of the LionsHead Integration Library, Source, Messenger, and Routing Rule are all pluggable via a HandlerFactory. The same engine ports to Teams, Discord, Webex, or any other surface by registering 1 new Messenger plugin. No rewrite of the routing core.

Tangible Result

Requests route to the first actually available practitioner in seconds, not minutes of shouting. Misrouted requests drop to near 0, because the system explains in plain English why a given routing isn't allowed. The channel stays clean, stale requests and their bot replies vanish on a daily cadence with a full audit trail. And because the architecture is a library rather than a one off, the next routing engine inherits all of this for free.

Problem

On a fast floor the scoreboard is always stale. A deal closes in the CRM at 10:41, the manager hears about it in a spreadsheet after lunch, coaches on it the next morning, and reconciles cost per sale at the end of the week. By then the moment has passed. The friction is not a missing tool, it is the gap between the tools. The dialer knows the calls, the CRM knows the sales, the finance side knows the lead spend, and no single instrument sees all 3 at once in time to act. Originally deployed against a paid lead insurance sales practice. The same gap opens on any high velocity floor where lead spend, live conversion, and recognition live in 3 separate places.

Architectural Solution

A headless, always on metasystem that reads fact and signal across the systems the practice already runs and turns it into a single live voice the whole floor hears. On a 20 second poll it recognizes every close the instant it is written and fans 3 concurrent voices out of 1 event, a card to the sales channel, a direct message to the closer, a direct message to the enroller who set it up. Every alert carries agency cost per sale live, plus a running watch list tracking cost per acquisition (CPA) that names the agents running hot and, for each, exactly how many more closes bring them back under target. Underneath it a peer conversion significance test decides who genuinely needs a hand rather than who simply drew an expensive lead. A pace and projection line closes each card, today against the same minute yesterday, recomputed on every sale so a midday slump pulls the number down honestly.

Tangible Result

It hands the week back. The manual round trip (pulling the report, tallying the day, chasing who's over on cost, reconstructing yesterday to know if today is good) disappears. In its place, a floor that recognizes itself in real time, 20 seconds from close to alert, a cost signal that arrives while it can still change an outcome, and a performance flag precise enough to trust with a person's confidence. Recognition reaches 3 voices at once (channel, closer, enroller) and 1 statistical flag replaces gut feel coaching. Beyond the wins, the full arc of the book arrives as its own threaded, deduplicated alerts, rejected and trashed policies, weekly performance rollups, deals waiting to be dropped. The manager stops assembling the picture and starts acting on it.

Problem

The moment your practice scales past you doing every conversation yourself, the question shifts. It's no longer 'am I good enough on this call.' It's 'is everyone in my practice good enough on every call.' That's a different problem. In regulated spaces (medical patient outreach, financial advisory recommendations, legal intake, insurance enrollment) it's the problem that closes your firm if you get it wrong. Every recorded conversation has to demonstrably clear a quality rubric, needs analysis, consent capture, scorecard threshold, disclosure handling. The AI scoring vendors that produce those scores each expose the data differently. The compliance templates that have to be populated all want the same data shaped slightly differently. Building a one off integration per vendor and template pair is how compliance ops drowns. Worse, when a scoring system updates a call's score days after the call closed, because a reviewer overrode a measure or because the rubric itself changed, there's no clean way to know which historical exports need to be reissued. Originally deployed against a Medicare Advantage scorecard. The framework applies to any regulated, phone based context.

Architectural Solution

A 4 category handler framework (Source, Destination, Messenger, and a category specific AIScoringHandler interface) wired together by a YAML config file. 1 deployment equals 1 YAML. The Source category abstracts every AI scoring vendor behind a single fetch_calls(start, end, **filters) interface that yields canonical Call+Score payloads, concrete handlers translate each vendor's wire format. The Destination category abstracts the output template (XLSX agency workbook, BI warehouse, CSV export), concrete handlers preserve template styling, sheet names, and cell formatting per row. A classified transport layer wraps every external call, TransientError, RateLimitError, AuthError, ClientError, ValidationError, each with its own retry policy, Retry-After header honored, exponential backoff with jitter on transient failures. A SQLite backed state ledger sits at the seam between source and destination. It stores each call's content hash and implements an upsert contract (insert, unchanged, updated) so late arriving score revisions automatically trigger the export again. Auth is itself pluggable (API key, basic, OAuth) so the same handler ports across an entire vendor category without an auth rewrite.

Tangible Result

A scoring and compliance pipeline that runs unattended overnight and lands a fully formatted carrier template export in the right folder by morning. Late score revisions from the AI vendor flow through the ledger and export again on the next cycle with 0 human intervention. Onboarding a new AI scoring vendor or a new compliance template is 1 handler module and 1 config block. Not a rewrite. The framework itself is the deliverable. Every subsequent compliance automation engagement inherits the transport, the ledger, the auth modules, and the abstract category interfaces for free.

Problem

The counterparty controls both the payment and the reporting. Their statement is the only account of what you are owed, their books are never shared, and the agency has no independent instrument, so every dollar that arrives and every dollar that does not is taken on faith. A reconciliation fed by that same counterparty inherits their version of events by construction, which means the 1 disposition that actually costs you, a policy that appears on no statement at all, is invisible to it permanently. Not a gap that closes in the next release, a property of where the data comes from. Anyone patient can find a payment that came up short. Nobody finds the payment that was never mentioned.

Architectural Solution

A lifecycle ledger where every policy carries 2 statuses that are never collapsed into 1. The position side tracks what the carrier says about the policy, the payment side tracks what the counterparty says about the money, and the records worth acting on are the ones where the 2 disagree. An active policy that was never advanced reads Missing. Once the advance lands it reads Advanced. At the configured maturity it flips to Reconcile, and if the pay date passes with no reconciliation line it moves to Audit, expected payment, not received. Statement files are then overlaid onto that computed book rather than replacing it.

Tangible Result

The question that never used to get asked, is anyone actually paying what they owe, now gets asked of every policy on every cycle. Omission becomes detectable, because the ledger is fed by a source the counterparty does not control. Flags arrive dated and suppressed down to the ones that survive scrutiny, so they can be handed to the counterparty rather than argued about internally. What the instrument found, and what came back, is the analytical half and lives in the Insights write up.