We're a senior U.S.-based engineering team specializing in the systems that keep modern businesses running — Salesforce custom development, system integrations, API architecture, and the production-grade infrastructure work that holds everything together. When AI is the right fit, we deliver that too: voice agents that handle thousands of calls, document AI that reviews contracts in seconds, and computer vision that catches what humans miss. You always work directly with the engineer writing your code.
Projects in the Showcase
Based Senior Engineers
Years Engineering
Our Core Specialty
Salesforce as your system of record, but data sitting in 8+ disconnected tools, reps re-keying everything, and finance reconciling by hand at month-end.
Need iOS and Android shipped from one codebase, store-review-ready on day one, with crash-free sessions and a release cadence both platforms actually keep in sync.
Need faster loan decisions, fraud detection that actually works, compliance monitoring, or accounts receivable that collects itself.
Missing calls, long hold times, inconsistent quality. AI agents that handle calls 24/7 and coach your team in real-time.
Contract review taking weeks, regulatory filings consuming staff, or compliance gaps keeping you up at night.
Inventory forecasting off, pricing leaving money on the table, or customer experience that can't compete with the big players.
OSHA fines, safety incidents, and site monitoring gaps. Computer vision that watches every worker, every site, every minute.
Donor CRMs that actually talk to your fundraising platform, grant reporting that writes itself, and lean automation for teams running real impact on small-team budgets.
Don't see your industry? We've probably solved something like your problem before.
Free strategy call to understand your business, identify the highest-impact AI opportunities, and assess feasibility. No commitment, no fluff.
We design the solution — selecting models, planning integrations with your existing systems, and defining success metrics before writing a line of code.
Our team builds, tests, and deploys your AI solution with regular demos so you see progress weekly — not just at the end.
Post-launch, we monitor performance, optimize based on real data, and scale what's working. Ongoing support, not a handoff and goodbye.
Every engagement starts with a free strategy call. No commitment required.
A living library of integration projects, Salesforce builds, AI systems, and SaaS products — reference implementations of everything we ship.
Practical AI insights, real-world lessons, and engineering strategies to help your business move faster.
Auditors and credit committees will reject vague metrics—this spec ties a KPI back to raw rows, transformations, and model versions using dbt + Snowflake + MLflow. Includes an acceptance‑gate checklist, vendor patterns (Databricks vs Snowflake+dbt), and a minimal telemetry plan to keep an auditable trail without exploding costs.
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Retraining Airewrite is an expensive, audit-heavy hammer — use this playbook to decide when retrain is actually necessary versus prompt fixes, retrieval tweaks, or preprocessing.
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Models don't stop fraud by themselves — deployable, auditable streaming pipelines do. This engineer-level playbook covers CDC, low-latency scoring, feature freshness, model serving, and an SLA template that maps to dollars saved.
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A practical, CFO-facing checklist for choosing a predictive maintenance system that drives measurable failure prevention, with sensor minimums, architecture patterns, ROI math, and a 6-point pilot contract.
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An opinionated CTO briefing comparing Delta Lake (Databricks), BigQuery, and Snowflake for enterprise MLOps — cost-to-production, latency, lineage, and auditability tradeoffs.
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A vendor-forward scorecard for AI credit underwriting: who to call (FICO, Zest AI, Experian partners, mid‑market platforms), realistic TCO line items, compliance gaps, migration checklist and contract clauses to insist on.
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Pick the MLOps stack that matches your data gravity and SLAs, not the coolest demo. This checklist contrasts Databricks, MLflow, Vertex AI, Snowflake and dbt with concrete architecture patterns, CI/CD playbooks, cost drivers and three contract clauses to insist on.
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If your AR team still chases invoices over email and phone, you’re leaving weeks of cash on the table. This playbook shows a focused AI collections stack — auto-dialers, intent classification, promise-to-pay forecasting, and smart disputes routing — that can cut DSO 10–25% in 90 days when built with audit trails, dispute explainability, and solid data contracts.
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