ZTABS develops AI agents for Seattle businesses operating at the intersection of cloud computing leadership and industry innovation — cloud infrastructure management agents for the hundreds of technology companies headquartered in Seattle and the greater Puget Sound region that run their operations on AWS and Azure (both headquartered locally) where autonomous agents monitor, optimize, and remediate cloud infrastructure across thousands of services, millions of resources, and monthly cloud bills exceeding $1M-$50M+ where even a 5% optimization represents hundreds of thousands in annual savings, supply chain orchestration agents for the massive logistics and retail ecosystem anchored by Amazon, Costco, Nordstrom, and the Port of Seattle where AI agents autonomously manage inventory, routing, demand forecasting, and vendor coordination across supply chains that stretch from Asian manufacturing to last-mile delivery in the Pacific Northwest, and healthcare AI assistants for Seattle's world-class medical institutions including UW Medicine, Swedish Medical Center, and Providence where AI agents support clinical decision-making, patient coordination, and research operations while navigating the strict regulatory requirements of HIPAA and FDA oversight.
AI agent development in Seattle serves a city that is uniquely positioned at the center of the cloud computing revolution. Seattle's AI agent landscape: Seattle is arguably the most important city in the world for cloud computing — Amazon Web Services (AWS) and Microsoft Azure, the two largest cloud platforms globally (together commanding 55%+ of the cloud infrastructure market), are both headquartered in the greater Seattle-Bellevue metro area. This concentration creates a unique AI agent development environment. First, Seattle businesses have deep cloud expertise — engineers in the Seattle market understand cloud architecture at a level that enables more sophisticated AI agent deployment than virtually any other market. Second, the cloud platforms themselves are rapidly building AI agent capabilities (AWS Bedrock Agents, Azure AI Agents), and Seattle developers have early access to these tools and direct relationships with the platform teams building them. Third, Seattle's cost of cloud operations is a primary business concern — technology companies headquartered here often run massive cloud deployments, and AI agents that optimize cloud spending have immediate, measurable ROI. Cloud operations complexity: a mid-size Seattle tech company might run 2,000+ microservices across 50,000+ cloud resources (EC2 instances, Lambda functions, S3 buckets, RDS databases, EKS clusters) on AWS, with a monthly cloud bill of $3-8M. Managing this infrastructure requires 24/7 monitoring, capacity planning, cost optimization, security posture management, and incident response. Traditional approaches use a combination of CloudWatch/Datadog for monitoring, manual runbooks for incident response, and periodic cost reviews. AI agents transform this from reactive management to autonomous optimization — agents continuously analyze infrastructure telemetry, predict capacity needs, identify cost optimization opportunities, detect and remediate security vulnerabilities, and respond to incidents with automated playbooks that execute in seconds rather than the minutes or hours that human response requires. Supply chain scale: Seattle's supply chain ecosystem is anchored by Amazon (the world's largest ecommerce and logistics company), Costco ($230B+ revenue), and the Port of Seattle (handling 3.5M+ TEUs annually as a primary US gateway for Asian trade). The supply chain complexity in the Seattle market is enormous — managing millions of SKUs, thousands of suppliers, hundreds of warehouses and fulfillment centers, and delivery networks spanning the continent. AI agents that can autonomously manage portions of this supply chain — predicting demand, optimizing inventory levels, routing shipments, and coordinating with suppliers — create value measured in billions across the Seattle supply chain ecosystem. Healthcare innovation: Seattle's healthcare sector includes world-class research institutions (Fred Hutchinson Cancer Center, Allen Institute, Institute for Systems Biology), major health systems (UW Medicine, Swedish/Providence, Virginia Mason Franciscan), and a growing health tech startup ecosystem. These institutions generate massive volumes of clinical and research data that AI agents can help manage — from patient scheduling optimization to clinical trial recruitment to research data analysis. Washington state's progressive health technology policies and the concentration of tech talent create an environment where healthcare AI agent adoption is faster than in most US markets.
Seattle AI agent demand spans cloud infrastructure, supply chain, healthcare, and enterprise operations. Cloud Infrastructure Agents: cost optimization agents (AI agents that continuously analyze cloud spending and identify optimization opportunities — right-sizing recommendations (identifying over-provisioned instances that can be downsized without performance impact), Reserved Instance and Savings Plan optimization (analyzing usage patterns to recommend optimal commitment purchases — a 1-year RI typically saves 30-40% vs. on-demand), unused resource identification (detecting idle databases, unattached storage volumes, and orphaned resources that cost money without providing value), and architecture optimization (recommending serverless alternatives for intermittent workloads, spot instances for fault-tolerant workloads, and storage tier optimization for infrequently accessed data) — for Seattle tech: cost optimization agents typically identify 15-25% savings on cloud bills — for a company spending $5M/month on AWS, this represents $750K-$1.25M in monthly savings — the agent continuously monitors for new optimization opportunities as infrastructure changes), incident response agents (AI agents that detect, diagnose, and remediate infrastructure incidents — detecting anomalies in metrics (latency spikes, error rate increases, resource exhaustion), correlating signals across services to identify root cause (a database connection pool exhaustion causing cascading failures across dependent services), executing automated remediation (scaling up resources, restarting services, failing over to healthy replicas, rolling back bad deployments), and communicating status to on-call teams with context and actions taken — for Seattle tech: incident response agents reduce Mean Time to Resolution (MTTR) from 30-60 minutes to 2-5 minutes for common incident types — reducing customer impact and on-call engineer burden), and security posture agents (AI agents that continuously assess and improve cloud security — scanning for misconfigurations (public S3 buckets, overly permissive IAM policies, unencrypted databases), monitoring for suspicious activity (unusual API calls, unauthorized access patterns, data exfiltration indicators), enforcing compliance (SOC 2, HIPAA, PCI-DSS — ensuring all resources meet required security controls), and automating remediation (automatically correcting misconfigurations before they can be exploited) — for Seattle tech: security posture agents provide continuous compliance monitoring that would require a team of 3-5 security engineers to perform manually)). Supply Chain Agents: demand forecasting agents (AI agents that predict product demand across channels, regions, and time periods — analyzing historical sales data, seasonal patterns, promotional calendars, market trends, competitor activity, and external factors (weather, economic indicators, social media trends) to generate demand forecasts at the SKU level — for Seattle retail: accurate demand forecasting directly impacts inventory costs (overstocking ties up capital, understocking loses sales) — AI agents that improve forecast accuracy by 10-15% can reduce inventory carrying costs by 20-30% while improving product availability), inventory optimization agents (AI agents that determine optimal inventory levels across the supply chain — how much of each product to hold at each location (warehouse, fulfillment center, store), when to reorder, and how to balance the cost of holding inventory against the cost of stockouts — for Seattle supply chain: inventory optimization across thousands of SKUs and hundreds of locations is a mathematical problem too complex for human analysis — AI agents process the data continuously, adjusting inventory targets based on real-time demand signals, supply lead times, and cost parameters), and logistics routing agents (AI agents that optimize shipment routing — selecting carriers, routes, and shipping methods that minimize cost while meeting delivery commitments — for Seattle logistics: routing optimization must consider Pacific Northwest geography (mountain passes, ferry schedules, seasonal weather), carrier capacity and pricing, last-mile delivery logistics in urban (Seattle, Portland) and rural areas, and cross-border shipments to/from British Columbia — AI agents evaluate thousands of routing options for each shipment, selecting the optimal combination)). Healthcare Agents: clinical workflow agents (AI agents that optimize clinical operations — patient scheduling (matching patients to providers based on condition, urgency, provider expertise, and availability), care coordination (managing referrals, follow-up appointments, and multi-provider care plans), and clinical documentation (AI-assisted note generation, coding suggestion, and documentation compliance checking) — for Seattle healthcare: clinical workflow agents reduce the administrative burden on clinicians — UW Medicine physicians report spending 2+ hours per day on administrative tasks that could be partially automated — agents handle scheduling optimization, routine documentation, and care coordination routing while clinicians focus on patient care), patient communication agents (AI agents managing patient communication — appointment reminders, pre-visit instructions, post-visit follow-up, medication reminders, and health education — communicating via the patient's preferred channel (SMS, email, patient portal, phone) in their preferred language — for Seattle healthcare: patient communication agents reduce no-show rates (a 2-3% improvement in show rates represents significant revenue recovery for health systems), improve medication adherence, and increase patient satisfaction — agents communicate proactively rather than waiting for patients to remember their responsibilities), and research support agents (AI agents supporting medical research — literature monitoring (continuously scanning PubMed and other databases for relevant publications), clinical trial recruitment (matching eligible patients to open trials based on inclusion/exclusion criteria), and data management (organizing research data, monitoring data quality, and generating interim analyses) — for Seattle research: Fred Hutchinson, UW Medicine, and the Allen Institute conduct thousands of concurrent studies — AI agents that accelerate literature review by 60-70% and improve trial recruitment rates by 20-30% significantly advance research productivity)).
Our AI agent development for Seattle builds autonomous systems that leverage the city's deep cloud infrastructure expertise and industry knowledge. Phase 1 — Agent Architecture (2-3 weeks): designing the AI agent system: use case definition (identifying specific tasks the agent will perform autonomously — for cloud: "The agent will monitor EC2 instance utilization across all accounts, identify instances with average CPU below 20% for 14+ days, recommend right-sizing options, and execute approved changes during maintenance windows" — precise use case definition prevents scope creep and ensures the agent does specific things well rather than attempting to do everything poorly), tool and API mapping (identifying the tools and APIs the agent needs — for cloud: AWS Cost Explorer API, CloudWatch APIs, EC2 APIs for instance management, and Terraform/CDK for infrastructure changes — for supply chain: ERP APIs, WMS APIs, carrier APIs, and demand data sources — for healthcare: EHR APIs (Epic FHIR), scheduling system APIs, and clinical data warehouse connections — each tool is evaluated for reliability, rate limits, and security), and safety and guardrail design (defining what the agent can and cannot do autonomously — for cloud: the agent can recommend right-sizing but requires human approval for changes affecting production workloads — the agent can automatically remediate non-critical incidents but must alert on-call engineers for critical incidents — guardrails prevent the agent from taking actions that could cause outages, data loss, or security violations)). Phase 2 — Agent Development (4-8 weeks): building the AI agent: core agent logic (developing the agent's reasoning and decision-making capabilities — for cloud optimization: the agent analyzes utilization metrics, correlates with application performance data, evaluates right-sizing options against performance requirements, and generates recommendations with confidence scores and risk assessments — for supply chain: the agent processes demand signals, evaluates inventory positions against service level targets, and generates replenishment recommendations optimized for cost and availability), tool integration (connecting the agent to the APIs and tools it needs — building robust API clients with authentication, error handling, rate limit management, and retry logic — for cloud: integrating with AWS/Azure management APIs requires careful handling of pagination, eventual consistency, and cross-region operations — for healthcare: integrating with Epic FHIR requires SMART on FHIR authentication and compliance with Epic's data access policies), and testing and simulation (testing the agent in simulated environments before production deployment — for cloud: replaying historical infrastructure data through the agent and comparing its recommendations against actual human decisions — for supply chain: simulating demand scenarios and evaluating agent inventory decisions against actual outcomes — simulation testing verifies that the agent makes good decisions before it is given authority to act)). Phase 3 — Deployment and Monitoring (2-4 weeks): putting the agent into production: shadow mode deployment (deploying the agent in "shadow mode" — the agent observes real data and generates recommendations but does not take action — its recommendations are compared against human decisions to validate accuracy — for cloud: the agent runs alongside the existing operations team for 2-4 weeks, with daily comparisons of agent recommendations vs. human decisions — when agent accuracy exceeds 90% on routine decisions, it graduates to autonomous mode for those decision types), autonomous deployment (transitioning approved decision types from shadow mode to autonomous execution — with monitoring dashboards showing every action the agent takes, the reasoning behind each action, and the outcome — for Seattle: we deploy with conservative guardrails initially, gradually expanding the agent's authority as confidence builds), and performance monitoring (continuous monitoring of agent performance — decision accuracy (are the agent's decisions producing good outcomes?), action success rate (are automated actions executing correctly?), and business impact (is the agent delivering the expected value — cost savings, time recovery, error reduction?) — monitoring ensures that agent performance remains high and catches degradation before it causes problems)).
Cloud Computing / Infrastructure Cost Management
A Seattle-based B2B SaaS company — providing data analytics software to enterprise clients — ran its entire platform on AWS across 3 regions (us-west-2, us-east-1, eu-west-1) with a monthly AWS bill averaging $4.2M. The infrastructure included 1,400+ EC2 instances (from t3.micro to p4d.24xlarge GPU instances for ML workloads), 280 RDS databases, 45 EKS clusters, 2,000+ Lambda functions, and 180TB of S3 storage across tiers. The cost management challenge: the company's cloud spending had grown 35% year-over-year while revenue grew 22% — cloud costs were growing faster than revenue, compressing margins. The CFO mandated a 20% reduction in cloud spend without impacting performance or reliability. The engineering team had attempted manual cost optimization — a quarterly "cost review" where engineers examined their services' resource usage and identified savings. Results: each quarterly review identified $80K-$120K in savings (2-3% of monthly spend) but consumed 200+ engineer-hours (engineers reviewing dashboards, analyzing metrics, and implementing changes). The savings were temporary — within 2-3 months, costs crept back up as new services were deployed without cost optimization, development environments were left running, and workload patterns changed. Specific challenges: (1) right-sizing was reactive — engineers only reviewed instance sizing when prompted, not continuously. Instances were typically provisioned for peak load and never downsized as workload patterns changed. The company estimated 25-30% of EC2 spend was on over-provisioned instances. (2) Reserved Instance coverage was suboptimal — the company had $1.8M in active RI commitments but coverage was only 42% (meaning 58% of eligible usage was billed at on-demand rates). Poor RI planning was driven by fear of over-commitment (buying RIs for instances that might not be needed) and the complexity of analyzing usage patterns across 1,400+ instances. (3) Development and staging environments ran 24/7 despite being used only during business hours (roughly 10 hours/day, 5 days/week) — these environments cost $680K/month, of which $480K was wasted on off-hours runtime. (4) Data transfer costs were $340K/month — largely driven by inefficient architecture (cross-region data transfer for workloads that could be co-located, large data pulls from S3 that could be reduced with better caching).
We developed an autonomous cloud cost optimization agent over 10 weeks that continuously monitored, analyzed, and optimized the company's AWS infrastructure. Agent Architecture: the agent operated as a set of specialized sub-agents coordinated by a central orchestrator. Right-Sizing Agent: continuously analyzed EC2 instance utilization (CPU, memory, network, disk I/O) over 14-day rolling windows. For each instance, the agent: calculated the optimal instance type based on actual utilization patterns (peak, average, P95), evaluated whether the workload was better suited for a different instance family (compute-optimized vs. general-purpose vs. memory-optimized), considered performance requirements (some workloads required consistent performance rather than average performance — the agent used P99 metrics for latency-sensitive services), and generated right-sizing recommendations with projected savings and risk assessment ("Downsize i-0abc123 from m5.4xlarge to m5.2xlarge — projected savings: $412/month — risk: LOW — CPU P95 utilization will increase from 34% to 68%, well within safe operating range"). The agent integrated with the company's deployment pipeline — approved right-sizing changes were executed during the next scheduled deployment window, with automatic rollback if performance metrics degraded. Reserved Instance Agent: analyzed usage patterns to recommend optimal RI and Savings Plan purchases. The agent: modeled usage patterns at the instance family level across all regions, simulated different RI/SP purchase scenarios (1-year vs. 3-year, All Upfront vs. Partial vs. No Upfront), recommended a purchase portfolio optimized for savings vs. flexibility, and monitored RI utilization — alerting when reserved capacity was going unused and recommending modifications or marketplace listings for unneeded RIs. Schedule Agent: identified resources that could be scheduled (started/stopped on a schedule) rather than running 24/7. The agent: analyzed usage patterns for all non-production resources (development, staging, QA, demo environments), identified access patterns (when were these environments actually used?), recommended schedules that covered all usage while minimizing cost (e.g., "Run dev-cluster-3 Mon-Fri 7AM-7PM PST — current 24/7 cost: $18,400/month — scheduled cost: $5,500/month — savings: $12,900/month"), and implemented scheduling through AWS Instance Scheduler with override capability (engineers could extend running hours when needed for late-night work or weekend deployments). Data Transfer Agent: analyzed data transfer patterns and identified optimization opportunities. The agent: mapped data flows between services, regions, and external endpoints, identified high-cost transfer patterns (cross-region transfers that could be eliminated by co-locating workloads, repeated S3 data pulls that could be cached), recommended architecture changes to reduce transfer costs, and monitored for new inefficient transfer patterns as the architecture evolved. Waste Elimination Agent: continuously scanned for unused or abandoned resources. The agent: identified unattached EBS volumes (storage volumes not connected to any instance), detected idle RDS databases (databases with zero connections for 7+ days), found orphaned Elastic IPs (allocated but unassigned public IP addresses — charged at $3.65/month each), identified unused Lambda functions (no invocations for 30+ days), and flagged stale S3 data (objects not accessed for 90+ days that could be moved to cheaper storage tiers). Each identified resource was verified against tagging and ownership data — the agent contacted the resource owner before recommending deletion, preventing accidental removal of resources that appeared unused but served a purpose.
The agent was deployed in shadow mode for 3 weeks (generating recommendations without acting), then transitioned to autonomous mode for approved action types over 4 weeks. Shadow Mode Results (3 weeks): the agent generated 2,840 recommendations during the shadow period. Engineering team reviewed a sample of 200 recommendations — 94% were validated as accurate and actionable. The 6% that were rejected were edge cases (instances with unusual burst patterns, databases used for monthly batch jobs that appeared idle). The edge cases were used to refine the agent's analysis logic before autonomous deployment. Autonomous Mode Results (first 6 months): Right-Sizing: the agent executed 380 right-sizing changes over 6 months (averaging 15/week). Zero performance incidents resulted from right-sizing — the agent's conservative approach (using P99 metrics and requiring a 30% headroom margin) ensured that downsized instances still had ample capacity. Right-sizing savings: $520K/month (12.4% of the original $4.2M monthly bill). Reserved Instance Optimization: the agent recommended a revised RI/SP portfolio — increasing coverage from 42% to 78% through strategic purchases of 1-year Convertible RIs (providing savings with flexibility to modify as needs changed). RI/SP savings: $380K/month (9.0% of the monthly bill). The agent continuously monitored RI utilization — when a Reserved Instance was underutilized (less than 80% for 14 days), the agent recommended modifications or marketplace listing. Scheduling: the agent implemented schedules for 340 non-production resources. Schedule savings: $480K/month (matching the pre-project estimate — development and staging environments that previously ran 24/7 now ran only during used hours with on-demand override capability). Data Transfer: the agent identified $180K/month in data transfer optimization opportunities — $120K was achieved through architecture recommendations that the engineering team implemented over 3 months, $60K was achieved through caching improvements the agent recommended. Waste Elimination: the agent identified and (after owner verification) removed: 240 unattached EBS volumes, 45 idle RDS databases, 180 orphaned Elastic IPs, and moved 42TB of stale S3 data to Glacier. Waste elimination savings: $140K/month. Total Savings: monthly savings: $1.7M (40.5% of the original $4.2M — exceeding the 20% mandate by 2x). Annual savings: $20.4M. The savings were sustained — unlike the temporary savings from quarterly manual reviews, the agent's continuous monitoring prevented cost creep. After 6 months, costs remained at the optimized level even as new services were deployed — the agent automatically right-sized new resources and identified optimization opportunities as they arose. Engineering Time Recovery: the quarterly manual cost review (200 engineer-hours per quarter) was eliminated — the agent handled cost optimization continuously. Engineers redirected this time to product development. Agent Development Investment: $340,000 (10 weeks of development and deployment). Annual operating cost: $42,000 (compute for the agent, API costs, monitoring). Year 1 net savings: $20.4M - $382K = $20.0M. ROI: 52x return on investment in Year 1.
Purpose-built AI agents that handle end-to-end business processes without constant human oversight.
Orchestrated teams of specialized agents that collaborate to complete complex workflows.
Agents that interact with APIs, databases, CRMs, and external services to execute real tasks.
Customer-facing agents that qualify leads, answer questions, and resolve support tickets autonomously.
Built-in observability, safety guardrails, and human-in-the-loop escalation for production reliability.
Agents that plug into your existing stack — Slack, Salesforce, HubSpot, Jira, and custom systems.
Seattle AI agent developers include cloud platform native tools, DevOps consultancies, and FinOps platforms. (1) Cloud native tools (AWS Trusted Advisor, Azure Advisor) — provide basic recommendations but lack the autonomous action, cross-service correlation, and continuous optimization that AI agents provide. (2) DevOps/SRE consultancies — skilled in infrastructure but often focused on reliability rather than the autonomous agent capabilities that enable continuous optimization. (3) FinOps platforms (CloudHealth, Spot by NetApp, Kubecost) — provide cost visibility and recommendations but typically require human action rather than autonomous execution. ZTABS builds autonomous AI agents that go beyond recommendations to take action — continuously optimizing cloud infrastructure, supply chains, and healthcare operations with the guardrails and monitoring that enterprise deployment requires.
AI agent development in Seattle varies by agent complexity, integration scope, and autonomy level. Advisory Agent (recommendations only, 4-6 weeks): $120,000-$250,000. Semi-Autonomous Agent (automated execution with approval gates, 6-10 weeks): $250,000-$500,000. Fully Autonomous Agent Platform (multiple agents, continuous optimization, 10-16 weeks): $500,000-$900,000. Ongoing: $10,000-$30,000/month for agent operations, monitoring, and evolution. ZTABS delivers at $220-320/hr — AI engineers with deep AWS/Azure expertise and Seattle industry domain knowledge.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract. We ship and operate our own software. 23 products in production.
Common questions about AI agent development for Seattle businesses
Traditional automation scripts follow fixed rules: "If CPU > 80%, scale up." AI agents reason about context: they analyze utilization patterns over time, consider the workload type, evaluate the impact on dependent services, and make nuanced decisions. A script scales up any instance hitting 80% CPU. An agent recognizes that a batch processing instance at 80% CPU is normal and will complete in 30 minutes, while a web server at 80% CPU during off-peak indicates a resource leak that needs investigation. Agents also learn from outcomes — adjusting their decisions based on what worked and what didn't.
ZTABS is a remote-first AI agent development agency serving Houston businesses — including custom autonomous agents, multi-agent systems, tool-using agents. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Agent Development in New York, NYZTABS is a remote-first AI agent development agency serving New York businesses — including custom autonomous agents, multi-agent systems, tool-using agents. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Agent Development in Los Angeles, CAZTABS is a remote-first AI agent development agency serving Los Angeles businesses — including custom autonomous agents, multi-agent systems, tool-using agents. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Development in Seattle, WAZTABS is a remote-first web development agency serving Seattle businesses — including full-stack development, progressive web apps, api development. We work with Cloud Computing, E-commerce & Retail Tech, AI & Machine Learning companies in Seattle, WA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Design in Seattle, WAZTABS is a remote-first web design agency serving Seattle businesses — including ui/ux design, responsive design, custom interfaces. We work with Cloud Computing, E-commerce & Retail Tech, AI & Machine Learning companies in Seattle, WA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in Seattle, WAZTABS is a remote-first AI development agency serving Seattle businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with Cloud Computing, E-commerce & Retail Tech, AI & Machine Learning companies in Seattle, WA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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