Real projects, real complications, real results. Presented without a single client name attached.
We don't name our clients. Not because we're hiding anything, but because their systems, their data structure, and their internal workflows aren't ours to show off. The same NDA we'd sign to protect your business is one we've already signed for theirs.
What follows are real projects, real problems, and real numbers. Just without the names attached.
Every client's systems live in their own isolated environment. We don't reuse a client's specific automation logic, prompts, or data structure for anyone else, not in aggregate, not anonymized, not as a "template" for the next project. What we build for one business stays with that business.
During a NetSuite implementation for a large retailer, prospecting for returning customers was still happening cold, the same outreach for someone who'd bought three times last year as someone who'd never purchased at all.
We built a dashboard surfacing customer purchasing patterns directly from the new system: when a customer historically buys, how much they typically spend, and how they pay. Instead of a generic call list, sales could prioritize outreach around a customer's actual buying rhythm, reaching people close to when they were likely to purchase again, not months off from it.
Customers reached through the targeted outreach returned at a 50% higher rate than those contacted the old way.
Still running outreach cold instead of targeted?
A 200-employee textile manufacturer had outgrown spreadsheets and was trying to get live reporting directly off their operational PostgreSQL database. It wasn't working, running analytical queries against a live transactional system slowed down the actual business software people depended on every day. Looking for a real fix, they'd started pricing out enterprise data platforms, Fivetran, Snowflake, dbt Enterprise, and found the cost and complexity genuinely alarming for a business their size, thousands of dollars a month, and infrastructure that would strain their existing legacy systems rather than work with them. The owner was frustrated and not sure there was a version of this that actually fit their business.
We didn't put them on the same stack a much larger company would use. We kept managed PostgreSQL as their transactional database, exactly what it's built for, but separated the analytics layer out entirely so heavy reporting queries never touch the live system again. For transformation, we used DuckDB, which runs in-memory on serverless infrastructure with no standing server cost, paired with dbt to write clean, modular, version-controlled SQL.
Google BigQuery still has a role, but not as the everyday engine. We use it strictly as long-term cold storage, a monthly archive push, so historical data stays searchable and ready for future analysis without paying to keep a massive warehouse running 24/7 for day-to-day reporting.
Because the transformation logic lives in dbt, it's self-documenting. If a new manager joins a year from now, they're not stuck guessing how a metric is calculated, they can open the documentation and see exactly how data moves from PostgreSQL through DuckDB to the dashboard.
A full modern data stack running for roughly $60 to $230 a month, against enterprise-equivalent pricing that would have landed between $1,200 and over $3,000 a month for the same job. Enterprise-grade speed and reliability, at a cost that actually made sense for a 200-person business, with none of the "one developer holds all the knowledge" risk that comes with undocumented custom pipelines.
Overpaying for a data warehouse you don't actually need yet?
Turning on Copilot for a large, multi-department workforce without first locking down file permissions is how employees end up accidentally seeing executive salaries and confidential data on day one. At enterprise scale, "just-in-time" permissions are almost always messy, and Copilot will surface anything it can technically reach.
Before a single license got assigned, we ran a full data governance and permission audit, deploying automated scanners to identify over-shared sites, sensitive executive folders, and outdated financial data, then re-scoping those permissions so the system only ever surfaced what a given user was authorized to see.
Sales and service needed fundamentally different builds. For sales, we deployed Copilot as a bridge between email, chat, and the CRM, so reps stopped manually updating pipeline stages, action items and follow-ups now generate themselves from a single prompt. For customer care, we anchored the assistant strictly to verified internal procedures and ticket history, not the open web, so a complex dispute surfaces the exact, correct resolution script in seconds.
Rolling this out to thousands of users only works if people actually use it. We staged the deployment in cohorts and built a champion network, training power users in every department to build workflow-specific prompts, while standing up a usage dashboard for leadership to track real adoption.
Sales reps stopped manually maintaining CRM records mid-conversation, resolution time on complex customer disputes dropped to seconds, and leadership had a real, measurable view of adoption, not just a license count.
Rolling out Copilot without a permission audit first?
A mid-sized county regulatory agency was buried under public records requests. The law sets strict response deadlines, but every step was manual: staff opened each email, worked out what the citizen wanted, searched decades of files on old local servers, pulled the matching PDFs, and redacted Social Security numbers, home addresses, and private medical details one click at a time. The backlog ran months, compliance penalties were a real risk, and staff were burned out. Enterprise government platforms were the obvious answer, but their licensing required a multi-year budget approval the agency didn't have time for.
Security came first. Government data can't go through public AI tools, so we built inside the agency's existing Microsoft 365 GCC environment, in an isolated, secure instance.
Then we automated the front end. Power Automate pulled requests from the public portal. A secure AI model read each one, categorized it, and searched the internal database for matching records.
We kept humans in charge of the release. Nothing leaves the agency without a human approving it. The AI pre-scans each file, highlights every Social Security number and address, applies a draft redaction layer, and routes the file to a supervisor's queue. Staff open a pre-processed document, verify the redactions in about ten seconds, and click Approve. The system then packages the response and emails the citizen securely.
Average response time dropped from 14 days to 45 minutes, including the supervisor's review. The backlog was cleared within a month. Staff went from twenty minutes of hunting and squinting per request to a ten-second review. The agency had been quoted $240,000 to $370,000 or more by a traditional enterprise vendor. We delivered this for $55,000, on infrastructure they already owned, with ongoing costs of roughly $3,000 a year.
Buried under a records-request backlog?
At enterprise scale, deep folder structures had become where information went to die. Finding an active HR policy could take twenty minutes, if the version someone found was even still current.
We moved the organization away from rigid folder hierarchies entirely, onto a flat architecture powered by structured metadata and a unified corporate taxonomy. We didn't just organize documents, we built a self-cleaning system: when a policy document hits a year old, it's automatically flagged for review and routed to its owner, then archived if it's not reapproved. Nobody sees outdated documentation by accident.
Relevance mattered as much as accuracy. We built audience targeting mapped to role, so a frontline employee logging in sees shift schedules and safety updates, while an executive sees performance metrics and compliance trackers, all from the same root URL. To stop uncontrolled site sprawl, we also built a custom provisioning portal, a manager requesting a new collaboration space fills out a short form, and the system verifies ownership and naming requirements automatically before provisioning it, with zero manual IT intervention.
Search time for critical documents dropped from roughly twenty minutes to seconds, with every employee seeing only current, compliant information, automatically enforced.
Drowning in folder chaos and outdated documentation?
A large national retailer migrating off legacy systems onto NetSuite, alongside a new warehouse management system and a last-mile delivery platform, all at once. The biggest issue wasn't any single system, it was the lag between them. An action in the warehouse system needed to reflect in NetSuite, and vice versa, and the volume of real-time data flowing between the two proved heavier than either system could sustain at launch.
We stepped back to hourly reconciliation while we solved it properly, a combination of scaling system capacity and reducing the volume of data moving through each sync. That workaround ran for two weeks. We didn't move back to real-time until we were completely certain the fix would hold.
That lag touched inventory accuracy, delivery scheduling, and the selling cycle while it was live. We'd recommended shifting go-live to a slower period to protect the customer experience during the transition. Leadership held the original date. We managed the impact as carefully as we could, but there were real failed deliveries and real customers affected during that window. We don't pretend otherwise.
What we controlled was the response. We stood up twice-daily calls, sales, customer care, and operations, open to any leadership who wanted in, and built daily microlearning modules for every leader involved, started before go-live and continued through the full resolution.
A fully resolved real-time sync within two weeks, an organization that stayed informed and trained throughout, and a hard lesson in what happens when a go-live date gets prioritized over a system's real capacity.
Facing a system migration with a tight go-live date?
On a manufacturing floor, every minute a critical machine sits broken destroys margin. A manual process, clipboards, phone calls, emails to a maintenance team, is too slow to catch that bleeding before it adds up.
We replaced the paper trail with a three-button mobile form. An operator taps the machine ID, selects the issue type, photographs the fault, and submits. From there, a single automated flow branches three ways at once: an instant, photo-attached alert to the maintenance team, an automatic flag in the production schedule so a broken machine can't be accidentally assigned new work, and a live inventory check for the failed part.
If the part isn't in stock, the system doesn't wait on a human to notice. It drafts a purchase request automatically. Below a set cost threshold, it approves and submits the order itself. Above that threshold, it escalates to the plant manager with the machine's downtime cost per hour shown right next to the part cost, approvable from a phone notification.
When the technician closes the ticket, the system calculates the full downtime duration automatically and logs it, feeding a live dashboard tracking mean time to repair, monthly cost of downtime, and which equipment fails most often.
What used to depend on someone finding the right person to call became a self-routing system, alerting the right team, protecting the schedule, and even purchasing its own replacement parts within a set budget, all without a single manual handoff.
Manual downtime tracking eating into your margins?
Sales and accounting systems that don't agree with each other create duplicate customers, mismatched revenue, and invoices nobody fully trusts.
An integration that runs without silent failures, duplicate customers, or a bookkeeper reconciling invoices by hand every month.
Tired of your systems arguing with each other?