# Aktagon – AI engineering for healthcare, finance & compliance > AI implementation and deployment for healthcare, finance, and compliance. From data readiness to production systems. --- ## About One engineer. No handoffs between a researcher, an architect, a developer, and a DevOps person. One person who stays accountable from first conversation to running system. I research, design, automate, and deploy AI systems for regulated industries — healthcare, finance, environments where compliance isn’t optional and demos don’t count. Shipping software since 2006. The lesson: most projects fail at handoffs, not at code. Fewer people, closer to the problem, with authority to decide. ## Products Healthcare and finance need AI that works with their data, not around it. Deploy AI Implementation The AI software is cheap. Making it work in a messy, real-world business is the hard part. Data readiness, system integration, production deployment. Dark Factory Autonomous AI agents that process documents, route data, and execute workflows without manual intervention. Scales without scaling the team. Data & search Clinical & Financial Data Most AI tools ignore the hard part: getting structured data out of messy clinical and financial systems. The model is only as good as what you feed it. Knowledge Search Your organization already has the answers — buried in PDFs, internal wikis, legacy databases. Search that surfaces them with citations, not hallucinations. Clinical Terminology MCP Verified medical coding — SNOMED CT, ICD-10, RxNorm — available locally inside your AI assistant. No data leaves your network. MLLP Server HL7 v2.x messages over MLLP, parsed and routed. One binary, no runtime dependencies, no integration engine license. Agents & automation AI Agents An agent without guardrails is a liability. Task-specific agents with defined boundaries, human-in-the-loop where it matters. No autonomous black boxes. Workflow Automation The bottleneck is rarely the algorithm. It’s the manual steps between systems — data routing, status checks, approval workflows. Who this is not for You need a team of ten. I am one engineer. If the project requires a department, this is not the right fit. You want a demo, not a product. I do not build proof-of-concepts that end at the slideshow. Production or nothing. You are not in a regulated industry. Healthcare, finance, compliance. If none apply, you will overpay for guardrails you do not need. You need 24/7 support SLAs. One person cannot promise round-the-clock availability. If uptime contracts matter, hire a team. One conversation. No handoffs. ### Dark factory. No humans required. A dark factory runs without the lights on. No one standing at the machine. The system was designed to handle it. AI agents work the same way — once deployed correctly, they process, route, and execute without someone watching every step. Not a chatbot. A worker. Most AI deployments put a human in front of a prompt. A dark factory puts agents behind the process. They read discharge summaries and extract medication changes. They parse financial filings and flag covenant breaches. They route lab results to the right clinician. Continuously. Without being asked. Autonomy with boundaries Autonomous does not mean unsupervised. Every agent operates within defined boundaries — scoped inputs, validated outputs, configurable approval gates. In regulated environments, certain decisions require human judgment by law. The system knows which ones and routes accordingly. Everything else runs. One engineer. Full system. Most vendors split this across teams — one group designs the agents, another integrates, a third handles deployment. By the time it reaches production, no single person understands the whole system. When something breaks at 3am, you’re filing tickets across three teams. Aktagon deploys the entire dark factory. One engineer — research through production. The person who designed the agent boundaries is the same person who monitors them in production. No handoffs. No diluted accountability. Deterministic orchestration for non-deterministic pipelines LLM outputs vary. That’s the nature of the tool. Regulated environments don’t accept “it depends.” Most teams let the model decide the workflow. That’s the mistake. A harness scopes every call — typed input, constrained output, validated transition. The model operates inside the harness. Not around it. The agent thinks freely. The system doesn’t. Example: a build pipeline where no agent evaluates its own work. graph TD A[Architect] -->|brief| H1[Human Gate] H1 -->|approved| B[Builder] B -->|diff| R[Reviewer] R -->|fail| B R -->|pass| H2[Human Gate] style A fill:#1a1a2e,stroke:#e94560,color:#eee style H1 fill:#1a1a2e,stroke:#533483,color:#eee style B fill:#1a1a2e,stroke:#0f3460,color:#eee style R fill:#1a1a2e,stroke:#16213e,color:#eee style H2 fill:#1a1a2e,stroke:#533483,color:#eee Scales without scaling the team The traditional model: more work requires more people. A dark factory breaks that constraint. The same agent deployment handles ten cases or ten thousand. The cost curve is flat where the headcount curve was steep. What ships Autonomous agent deployment for document processing, data routing, and workflow execution. Decision logging and audit trails. Approval gates where compliance requires them. Monitoring that flags anomalies. Built on open source foundations and twenty years of shipping production systems. A system that works while you sleep. One conversation. No handoffs. --- ### You cannot 1-click install AI into a messy business. Most AI tools assume clean data, modern APIs, and a team that knows what to do. Most businesses have none of those. The software is $20 a month. Making it work costs ten times that — because the real work is everything the vendor skips. Data readiness comes first 60% of UK businesses cite limited AI skills as a barrier to adoption. 80% neither use nor plan to use AI. Before deploying anything, I look at where the data actually lives — HL7 feeds from a lab system that drops fields, a CRM with 15 years of free-text notes nobody cleaned, PDF reports that were never digitized. Then I determine whether AI can work with what exists today or whether the plumbing needs fixing first. No one benefits from automating garbage. Integration is the expensive part A language model that cannot reach your data is a toy. I connect AI to the systems you actually run — EHR platforms, financial databases, legacy CRMs, file shares full of PDFs. HL7, FHIR, MLLP, proprietary exports. The integration respects your existing permission model, audit requirements, and data residency rules. Production deployment, not a proof of concept A demo that works on sample data proves nothing. I deploy where the real data lives, with real users, under real compliance constraints. Monitoring from day one — not as an afterthought, but as part of the system. If something drifts, you know before it becomes an incident. What ships Data readiness assessment with a clear go/no-go. System integration between AI and your existing tools. Production deployment with monitoring, logging, and audit trails. A running system, not a slide deck. One conversation. No handoffs. --- ### An agent without guardrails is a liability. Most agent demos impress. Most agent deployments fail. The gap is constraints. An autonomous system that can do anything will eventually do the wrong thing. I design task-specific agents with defined boundaries — scoped to one job, auditable at every step. Narrow scope beats general purpose A general-purpose agent sounds powerful until it hallucinates a clinical recommendation or approves a transaction it shouldn’t. Task-specific agents take structured inputs, follow defined logic, and produce validated outputs. They do one thing. They do it reliably. That’s the point. Human-in-the-loop is a design choice Not a fallback. Not a workaround for an agent that isn’t good enough. In regulated environments, certain decisions require human judgment by law. I design the handoff points intentionally — where the agent presents options and a human decides — so compliance is built into the workflow, not bolted on. What ships An agent that receives a discharge summary and extracts medication changes. One that reads a financial filing and flags covenant breaches. Each knows exactly what it accepts and what it returns. Decision logging for audit trails. Configurable approval gates. Monitoring that flags anomalies before they become incidents. One conversation. No handoffs. --- ### The bottleneck is rarely the algorithm. It’s the manual steps between systems. A clinician copies a lab result from one screen to another. A compliance officer re-enters data from an email into a spreadsheet. The algorithm works fine. The process around it is held together by copy-paste and hope. The expensive part isn’t the model It’s the forty minutes a day your team spends routing data between systems that don’t talk to each other. I automate the connective tissue — data routing, status checks, approval workflows — so your people make decisions instead of doing data entry. Automation that respects the chain of command Regulated industries have approval chains for a reason. I don’t bypass them. I automate everything around them: routing the right data to the right approver, tracking status, escalating when deadlines pass, logging every step for the audit trail. The human still decides. The system makes sure they decide on time, with the right information. What ships System integration between EHR, analytics, and reporting tools. Automated routing of messages, results, and alerts. Approval workflows with escalation rules. Audit logging that satisfies compliance review without manual assembly. One conversation. No handoffs. --- ### The model is only as good as what you feed it. Most AI projects skip the hard part. They start with the model. The model is the easy part. The hard part is getting structured data out of messy clinical and financial systems where formats change without warning and compliance isn’t optional. Regulated data has its own physics HL7 v2.x messages arrive malformed. FHIR R4 resources miss required fields. Proprietary EHR exports use undocumented delimiters. Financial feeds switch schemas between versions. I engineer pipelines that parse, validate, and transform all of it into clean, queryable data — regardless of what the source system decides to do today. Garbage in, expensive garbage out A model trained on dirty data doesn’t give wrong answers occasionally. It gives wrong answers confidently. In healthcare, that’s a patient safety issue. In finance, that’s a compliance violation. The pipeline is not infrastructure work you do before the real project starts. The pipeline is the project. What ships Data ingestion from HL7, FHIR, MLLP, and proprietary formats. Validation against regulatory schemas. Transformation into structures your models and analytics can actually use. Monitored, logged, auditable. One conversation. No handoffs. --- ### Your organization already has the answers. They’re buried in PDFs, internal wikis, legacy databases, and filing systems nobody maintains. Most companies respond by buying new tools. The better move is making existing knowledge findable. Citations, not hallucinations In regulated industries, an answer without a source is a liability. I deploy retrieval-augmented search that returns answers with exact citations — document name, page number, passage highlighted. Auditors don’t accept “the AI said so.” They accept a traceable chain from question to source. Your data stays yours No training on your documents. No sending sensitive clinical or financial data to third-party APIs you don’t control. Search runs on infrastructure you own, with access controls that match your existing permission model. What ships Semantic search across documents, wikis, and databases. Source attribution on every answer. Role-based access controls. Works with the formats you already have — PDF, DOCX, HTML, structured records. Deployed where your compliance team can audit it. One conversation. No handoffs. --- ## Contact You talk to the person who researches, designs, automates, and deploys. Same person. Start to finish. Tell me what needs to ship. Email Schedule a call