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.
Running Airewrite over PHI/PII or legal workflows with default settings will fail an auditor. This playbook gives engineers concrete patterns — data residency, on‑write redaction, encrypted vector stores, provenance bundles, and SIEM-linked canary evidence — plus implementation notes for Pinecone/Redis and Snowflake/BigQuery.
Read MoreA sloppy RAG deployment is an audit time bomb. This playbook gives legal, finance, and healthcare teams a pragmatic engineering design for deterministic citations, vector DB choices (Pinecone, Milvus, Elasticsearch), immutable provenance, and SLOs that stand up under subpoena.
Read MoreMost predictive‑maintenance pilots fail because teams pick the wrong model for the wrong failure mode and never measure avoided downtime. This post is a procurement + engineering scorecard: when to buy Siemens/Uptake, when to run Databricks + MLflow in‑house, and when AWS Lookout/Azure Predictive Services are the practical choice for mid‑market factories.
Read MoreBuying the wrong AI underwriting product costs months and millions. This CTO brief names the three vendor classes, the common TCO traps, and an 8‑point checklist to get auditable decisions in 8 seconds.
Read MoreIf you bill Airewrite (or any hosted LLM product) per-use, missing per-tenant controls will bleed margin and invite churn. This playbook gives engineering configs for per-tenant SLOs, throttles, cost attribution, soft vs hard token quotas, mirrored canaries, redrive strategies, and instrumentation using GCP/Vertex + Pinecone + Cloud Functions, Datadog, and Snowflake.
Read MoreEngineering field guide to what dataset sizes actually move the needle for visual inspection AI, where to spend labeling dollars, vendor and hardware tradeoffs, and realistic on-line accuracy ranges.
Read MoreIf Airewrite is in your stack, audit trail, canary evidence, and a redrive plan matter more than single-run model accuracy. This is a narrow engineering playbook for mirrored canaries, automated redrives, and multi-model fallbacks that preserve metric lineage and auditability.
Read MoreMigrations to or from Airewrite fail because teams skip canaries, token‑spend alarms, and hallucination regression tests. This playbook gives the operational controls you need: canary traffic splits, K8s manifests, hallucination detectors, token-budget alerts, rollback triggers, and a post‑mortem template.
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