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.
Niche medical‑scan pilots stall for three engineering reasons—not model size. This post gives three deploy‑first fixes, vendor-tested configs (NVIDIA Clara, Vertex AI, Databricks), and a CFO-friendly ROI worksheet that converts fewer false negatives and faster inference into scans/hour and revenue/procedure.
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If you can't give Procurement a dollar number and two KPIs in the first meeting, your AI receptionist is a dead pilot. This playbook supplies the event model, exact Salesforce field mappings, SQL for attribution, vendor notes (Twilio, Dialogflow CX, Amazon Connect, Service Cloud Voice), and a one-page CFO evidence bundle you can drop into procurement.
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If you can't forecast next quarter's inference bill, Airewrite isn't in production — it's a financial surprise. This playbook shows CFOs and CTOs how to model Airewrite licensing plus OpenAI/Anthropic/Vertex inference, where caching/quantization/prompt engineering cut token spend ~60%, which vector DBs actually save money at scale, and a sample TCO spreadsheet tied to SLOs and dollars.
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A CFO-ready playbook that forces the math on replacing rules with ML: break-even formulas, uplift measurement, audit-safe deployment patterns (Kafka + Snowflake + MLflow), vendor timelines.
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Agent coaching only pays when latency is under 200ms, the assist UI stays out of the agent's way, and outcomes are measured in seconds saved per call; this post gives production architectures, SLO budgets, ROI metrics, and a vendor map that works in live contact centers.
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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.
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A 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.
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Most 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.
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