Naysayers will have you believing AI doesn't work, is too expensive, makes too many mistakes. The doers are out there implementing solutions with it. The naysayer complaints come down to one thing: trust. The doers recognize the lack of trust as an engineering problem and are building governance to address the issue. In this post we share how Vixul portcos are handling the governance question as they implement AI in production.
EzOps: an AI agent inside client infrastructure
Thiago Maior's EzOps runs ACE, an agentic AI cloud engineer that works directly in a client's DevOps environment: Azure, Kubernetes, CI/CD, monitoring, Slack. It does what a junior platform engineer does: watch the systems, act, keep the pipeline moving.
The trust problem is obvious: nobody hands an unproven agent write access to production. EzOps engineered around it. Every automation is scoped to a documented scenario. Responses are cached where the model drifts. The agent runs in a sandbox and earns access in stages: observability first, read-only before it can act.
That discipline is the point, not a hedge. EzOps found that clients do not want an AI assistant to chat with. They want automations they can trust to run. The guardrails are what let the agent operate unattended in a live environment. Read more in EzOps' write-ups on human-centered AI for DevOps and AI as an engineering practice.
Stormatics: AI around the database, not in it
Umair Shahid's Stormatics runs PostgreSQL in production for fintechs under PCI DSS, SOC 2, and ISO 27001. A bad change at scale is an outage.
The trust failure is what happens without governance: clients let their teams loose with AI and end up with "cowboys in production" crashing the database at 3am. Stormatics gets the call to fix it. Their fix is to engineer the boundary. The database stays under human control, and AI is pointed at the jobs it can be trusted with: analyzing the workload, proposing index changes, and running health checks that never touch production on their own, with the DBA deciding what to apply. Governance is what turns "AI makes mistakes" into "AI does the safe work, humans hold the risk." Read how Stormatics does this in their posts on AI-assisted index tuning and MCP-based health checks.
Bitsol: an agent pipeline that ships HIPAA software
Javeed Muhammad's Bitsol builds healthcare apps that must be HIPAA-compliant on day one. The trust bar is a real auditor, not a skeptic.
So the governance is the pipeline. Their Prototype-to-Production engine is a stack of agents covering HIPAA compliance, stress testing, penetration testing, documentation, and handoff, with human review for the edge cases. Compliance runs at every stage, not as a final gate. That is what lets Bitsol take a client from a low-code MVP to an application a hospital will run, in weeks. The output is trusted because the process is built to be.
Enkefalos: private LLMs inside regulated enterprises
Enkefalos, founded by Lokesh Ballenahalli, deploys AI where a public API is a non-starter. Its Gen AI Foundry runs private, domain-tuned models inside the enterprise's own boundary, behind private endpoints and role-based access, with the data, training, and serving planes kept separate.
Governance is built into the architecture: compliance is applied at each runtime decision, every decision is logged with its inputs, outputs, and reasoning, and guardrails, responsible-AI checks, and a human in the loop bound what the model can do.
The founder's research explains why that matters. Lokesh Ballenahalli has co-authored studies showing LLMs stay highly sensitive to input noise and less predictable as task complexity and temperature rise. If the model itself is fragile, trust has to come from the system around it.
Prokopto: bringing AI into compliance
For Prokopto, founded by Naren Ravilla, the trust gap is the whole business. It runs managed security and compliance operations for startups and delivers audits as a service: readiness assessments and managed programs for SOC 2, HIPAA, ISO 27001, and now ISO 42001, the world's first AI management system standard.
ISO 42001 is not a point-in-time checklist. It is a full management system for AI: continuous risk management, AI impact assessments, lifecycle governance from concept to operation, and oversight of third-party models, all run on a plan-do-check-act loop.
That is the bet. As every client becomes an AI client, the compliance surface shifts from securing infrastructure to governing models, and Prokopto is building the capability to certify it. Its security operations run on the same principle: its managed detection and response is AI-powered, using models to detect, analyze, and respond to threats in real time. Prokopto uses AI to run security, and governs AI so its clients can ship it.
Every Business Will Be an AI Business
AI is not just for Silicon Valley tech companies. It is for real businesses, providing real value. This article showed how Vixul portcos are tackling the governance question driving the skepticism of established businesses. Every business will need to be an AI business in the coming years, and AI-native services companies will play a critical role in making that happen.
If you are the founder of a tech services company, come join our Round Table to learn how other founders are using AI to break into new markets where newcomer services firms have struggled in the past.