Agentiks Enterprises architects and ships the systems modern businesses run on — API-led integration platforms proven at billions of events a day, and agentic AI systems designed, hardened, and operated in production. Not a slide deck. Not a pilot that stalls after the demo. Systems that ship, and that someone stays on the hook to run.
Before "agentic AI" was a category, this practice was architecting the integration platforms that Fortune 500 companies actually run their operations on — the kind of systems that don't get a second chance when they fail at 2am. That discipline is what now goes into every AI system we build: grounded, observable, and built to survive contact with production.
Owned architecture and delivery of 200 APIs connecting 20 backend systems across North America, Europe, and Latin America, sustaining peak throughput of 1,100 transactions per second at sub-second latency against a 99.95% uptime SLA.
Diagnosed and resolved recurring production outages caused by redundant messaging exhausting ERP API quotas of up to 1M calls a day, redesigning the integration to restore stable operations and lower the customer's consumption costs.
Designed and shipped an agentic AI capability that automatically ingests roughly 1,000 invoice documents per day into CRM, eliminating manual data entry and accelerating downstream processing.
Sole solution architect on Accenture's DHA capture effort. Architected a real-time, Kafka-based integration layer letting independent military training systems and an AWS-hosted records platform exchange learner data continuously.
Architected the API platform behind Chicago's contact tracing and vaccine management programs, securely delivering immunization data to EHR systems and state registries across Illinois, Utah, Texas, and Nevada.
Led a six-person data engineering team building serverless IoT processing on Kinesis, Lambda, and S3, migrating the data warehouse to Redshift and re-engineering ETL to power real-time fraud detection.
The same hands that design your systems ship and run production AI products of our own. That's not a portfolio piece — it's proof that what we recommend has already survived contact with real users, real cost pressure, and real failure modes.
A multi-model AI content engine (Claude, Grok, Gemini, GPT) that turns live data into scripted, fact-checked, multilingual video, audio, and social content, published automatically. Runs a five-stage signal chain with a RAG grounding layer and an LLM-as-judge evaluation loop, unattended, with no human review. Shipped 200+ videos and grew to 7,000+ subscribers in 45 days, proven across the 2026 World Cup.
A consumer AI song creator — text-to-song in 12 languages, orchestrating four models across two modalities with an automatic failover chain, layered safety guardrails, and prepay-then-spend cost controls that fail closed rather than open-ended retry. A double-charge race was eliminated by moving the one-song-at-a-time guarantee into the data layer itself.
A live sports intelligence platform covering nine major leagues — Premier League, La Liga, Serie A, Bundesliga, Champions League, Liga MX, Brasileirão, Liga Profesional, and Liga BetPlay — with real-time scores, standings, and player ratings. Gemini-powered analysis auto-generates match infographics and scores highlight clips for relevance as the game happens, not in a next-day recap.
Every engagement draws on the same core competencies — whether the deliverable is an integration platform, an agentic AI system, or both working together.
Multi-model orchestration, RAG grounding, LLM-as-judge evaluation, and agentic design patterns — tool use, reflection, planning, multi-agent — built to run unattended, not demoed once and shelved.
Bidirectional integrations with ERP, finance, HR, and payment systems, delivered with reusable, API-led connectivity instead of brittle point-to-point connections.
Real-time, event-driven architectures that let independent systems exchange data continuously at scale, replacing batch and point-to-point patterns.
Platform architecture across AWS, Azure, GCP, and Cloudflare, chosen for the workload rather than a single vendor's roadmap.
Guardrails, cost controls, and evaluation built into the architecture before a system ever touches spend or customer data — not added after an incident.
Monitoring, alerting, and SLA measurement designed in from the start, proven at sub-second latency and 99.95% uptime on live enterprise traffic.
Standing up a Center for Enablement to drive platform adoption across teams — reusable templates, reference architectures, and CI/CD blueprints, not tribal knowledge.
Engagements delivered inside compliance and security constraints across healthcare, financial services, energy, and government — not designed around them after the fact.
Whether the deliverable is an integration platform or an agentic AI system, the discipline is the same: understand the real system first, design the guardrails before the feature, and stay on the hook after go-live.
Assess the systems you actually have, and define the target architecture and AI strategy against real constraints, not a greenfield assumption.
Grounding, evaluation, and cost controls are part of the design, not a patch applied after a pilot breaks in front of a customer.
Built and tested against your real systems and data, not a demo environment that quietly diverges from production.
Observability, failover, and SLAs proven under real load before go-live — the same bar this practice has shipped against for 20 years.
Production ownership and iteration as models and requirements change — the work doesn't end at the handoff meeting.
Replace brittle point-to-point connections with a platform. Reusable, API-led connectivity that lowers operating cost and outage risk instead of adding another one-off integration to the pile.
Delivery that respects the constraints. Healthcare, financial services, energy, and government engagements built inside compliance and security requirements from day one.
Agentic AI that runs unattended, in production. Guardrails, evaluation, and cost controls built in, so the system that impressed the room in the demo is the same one running six months later.
An architect who ships the code, not just the diagram. Hands-on, end-to-end ownership from design through production operation.
Whether it's an integration platform that's outgrown its architecture or an agentic AI idea stuck at the pilot stage, reach out and we'll walk through what shipping it for real would take.
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