# Automaark: Engineering Intelligent Systems. Automaark is a systems architecture and engineering firm. We design, build and operate production-grade software, AI and agentic systems, data platforms, cloud infrastructure, cybersecurity and growth systems for companies in the United States, Canada, Australia and worldwide. ## Facts - Name: Automaark (also: Automaark Digital Solutions) - Founder & CEO: Olamide Dada - Headquarters: New York, NY, United States - Roots: African roots, global team - Engineering team: North America, Europe, Africa, Asia - Primary markets: United States, Canada, Australia, Worldwide - Website: https://automaark.com - Contact: https://automaark.com/contact - Profiles: https://www.linkedin.com/company/automaark/, https://www.instagram.com/automaark - Not affiliated with: "Automark", "Automark Solutions", "Automark Agency" or any similarly named company. ## Thesis: Anyone can generate an app now. Very few can architect a system. AI has made it trivial to spin up a prototype. It has not made it any easier to design a system that survives real users, real data, real money and real attackers. That gap is where most AI-generated software fails: no data model, no security boundary, no observability, no plan for what happens at ten times the load. Automaark exists for that gap. We are architects and engineers first. We think in data flows, trust boundaries, failure modes and cost curves, then we build with whatever tools get the job done, including AI. The result is infrastructure a company can run on for years, not a demo that has to be rebuilt in six months. - Not "Generated in an afternoon" but "Designed for a decade" - Not "Works on the happy path" but "Handles failure, load and abuse" - Not "One person knows how it works" but "Documented, tested, transferable" - Not "Costs explode at scale" but "Cost curves modelled up front" ## Services (7 departments) ### Software & Platform Engineering URL: https://automaark.com/services/software-platform-engineering We build the software companies run on: multi-tenant SaaS products, two-sided marketplaces and booking platforms, operational platforms, customer portals and the APIs that connect them. Every system starts with an architecture pass: the data model, the API contract, the authentication and authorisation model, the deployment topology and the cost curve. Much of the hardest work is invisible: background services, job queues, schedulers, payment and payout flows, search, notifications, and the integrations that keep a platform in sync with the outside world. We design those as first-class parts of the system, not afterthoughts, because that is where marketplace-scale platforms succeed or fail. Web, mobile and partner integrations all consume the same API, so nothing is built twice and nothing drifts. Testing, documentation and runbooks ship with the code, which means your own team, or any team after us, can operate and extend it. Deliverables: - Architecture and data-model design, with decision records - Multi-tenant SaaS platforms and internal tools - Marketplaces, booking and reservation systems, payments and payouts - Background services: job queues, schedulers, workers, event pipelines - REST and GraphQL APIs, webhooks and partner integrations - Web applications (Next.js, React, TypeScript) and mobile (React Native, Flutter) - Backend services in Node.js, Python and Go - Legacy modernisation and re-platforming Typical stack: TypeScript, Next.js, React, Node.js, Python, Go, PostgreSQL, Supabase, Drizzle Q: Do you build the whole product or just parts of it? A: Either. We take products from architecture to production as the full engineering team, and we also join existing teams to own a specific layer such as the backend, the data platform or the integration layer. The scope is written down before work starts. Q: Who owns the code? A: You do. Code lives in your repositories, infrastructure runs in your cloud accounts, and credentials belong to you. We work in the open and hand over documentation, so there is no dependency on Automaark to keep the system running. Q: Can you build a marketplace or booking platform? A: Yes. Two-sided marketplaces and booking systems are among the most demanding products to build well: listings and availability, search, pricing, payments and payouts, messaging, reviews, fraud controls and the background services that keep it all consistent. We have built this class of platform, including in hospitality, and we design them for the scale patterns the large marketplaces established rather than as a CRUD app with a booking form. Q: Do you build background services and job systems? A: Yes, and we treat them as core architecture. Queues, workers, schedulers, retries, idempotency, dead-letter handling and observability are designed up front, because most production incidents in a platform come from the background, not the UI. Q: How do you handle an existing codebase? A: We start with an audit: architecture, dependencies, security posture, test coverage and the cost of change. You get a written assessment before we touch anything, and a plan that ranks fixes by risk and value. ### AI & Agentic Systems URL: https://automaark.com/services/ai-agentic-systems AI agents are useful when they can act inside a business: read the right data, call the right systems, and hand off to a person when they should. That takes more than a prompt. It takes retrieval over your own data, tool integrations with proper permissions, evaluation so you know when quality drops, and cost controls so an inference bill never surprises you. We design agentic systems as systems: identity and permissions for every agent, logging and audit trails, human approval where the stakes are high, and a clear line between what the model decides and what the code enforces. Deliverables: - AI agents and multi-agent workflows with tool use and approvals - Retrieval-augmented generation (RAG) over documents, databases and APIs - Model evaluation, monitoring and regression testing - Fine-tuning and prompt systems where they beat off-the-shelf models - Guardrails: permissions, rate limits, content controls, audit logs - Inference cost modelling and optimisation Typical stack: OpenAI, Anthropic, Open-weight models, LangGraph, pgvector, Python, TypeScript Q: What is the difference between an AI agent and agentic AI? A: An AI agent performs a defined task when asked: classify a ticket, draft a reply, extract fields from a document. Agentic AI is a system of agents that pursues a goal across several steps and systems, decides what to do next, and coordinates with other agents and people. Most companies should start with well-scoped agents and grow toward agentic workflows as trust and tooling mature. Q: Do you build custom models or use APIs like OpenAI and Anthropic? A: Both, chosen per problem. For most business use cases a frontier model behind a well-designed retrieval and tool layer beats a custom model on cost and time to value. We fine-tune or run open-weight models when data privacy, latency, volume or cost make it the better trade. Q: What happens when the AI gives a wrong answer? A: We design for it. Every agent has defined boundaries, cited sources where possible, confidence thresholds, and a human-in-the-loop step for actions that matter. Evaluation suites run on every change so quality regressions are caught before users see them. Q: How do you keep our data private? A: Data stays in your environment. We use enterprise API tiers with no-training terms, or self-hosted models inside your cloud when required. Access is scoped per agent, logged, and reviewable. ### Automation & Orchestration URL: https://automaark.com/services/automation-orchestration Most companies run on a web of manual steps between tools: a form here, a spreadsheet there, a person copying data between systems. We replace those with orchestrated workflows that trigger on events, handle failures, retry safely and leave an audit trail. Where a low-code tool such as n8n or a CRM automation is the right fit, we use it and document it. Where it is not, we build the orchestration in code. The test is the same either way: it has to run unattended for years. Deliverables: - Workflow design and process mapping - Event-driven orchestration (queues, schedulers, webhooks) - n8n, Make and custom workflow engines - Back-office automation: onboarding, billing, reporting, compliance - Document automation with AI extraction - Monitoring, alerting and failure handling Typical stack: n8n, Temporal, BullMQ, Node.js, Python, Webhooks Q: Which processes should we automate first? A: The ones that are frequent, rule-based and expensive when they go wrong: onboarding, invoicing, data entry between systems, reporting, compliance checks. We map the process, measure the current cost, and automate in the order that pays back fastest. Q: Will automation break when one of our tools changes? A: Well-designed automation is built around contracts and monitored. When a tool changes its API, the workflow fails loudly, retries where safe, and alerts someone. We build that in from the start rather than discovering it in a silent failure months later. ### Data Platforms & Indexing URL: https://automaark.com/services/data-platforms AI is only as good as the data it can reach. We design the data layer underneath: clean models, reliable pipelines, a warehouse or lakehouse when the business needs one, and indexing so that search, analytics and AI retrieval all read from the same trusted source. We are also building our own data indexing layer as an internal product, which keeps our client work grounded in what actually holds up at scale. Deliverables: - Data modelling and governance - ETL / ELT pipelines and change-data-capture - Warehouses and lakehouses (PostgreSQL, BigQuery, ClickHouse) - Search and vector indexing for AI retrieval - Analytics, attribution and reporting layers - Data quality monitoring Typical stack: PostgreSQL, BigQuery, ClickHouse, dbt, Dagster, pgvector, Elasticsearch Q: Do we need to clean our data before starting an AI project? A: Not all of it. We pilot on the data you have, measure where quality actually limits results, and fix those sources first. Trying to clean everything before starting is how AI projects stall for a year. Q: What is a data indexing layer? A: A system that takes data from many sources, structures it, and makes it queryable fast: by people through search and analytics, and by AI through retrieval. It is the difference between an AI that answers from your real records and one that guesses. ### Cloud & Infrastructure URL: https://automaark.com/services/cloud-infrastructure We design and run cloud infrastructure for the systems we build and for systems built by others: AWS, Google Cloud, Azure and modern platforms such as Railway and Vercel, chosen for the workload rather than by habit. Every environment gets infrastructure-as-code, CI/CD, monitoring, backups and a cost model. We treat the cloud bill as an engineering metric, because at scale it is one. Deliverables: - Cloud architecture and migration - Infrastructure as code (Terraform, Pulumi) - CI/CD pipelines and environment strategy - Containers and orchestration (Docker, Kubernetes) - Observability: logging, metrics, tracing, alerting - Cost optimisation and FinOps Typical stack: AWS, Google Cloud, Azure, Railway, Vercel, Terraform, Docker, Kubernetes, Cloudflare Q: Which cloud should we use? A: The one that fits your workload, team and compliance needs. For most early-stage products a managed platform is cheaper and faster than raw AWS; for regulated or high-volume systems the hyperscalers earn their complexity. We give a written recommendation with the cost model behind it. Q: Can you take over infrastructure someone else built? A: Yes. We start with an audit of the environment, access, backups, security groups and spend, then stabilise before changing anything. Documentation and runbooks are part of the handover. ### Cybersecurity URL: https://automaark.com/services/cybersecurity Security is an architecture property, not a product you bolt on. We design trust boundaries, identity and access, secrets handling and network topology into every system, and we review systems built by others for the gaps that attackers actually use. When something does go wrong, we run the response: containment, forensics, remediation and the documentation insurers, regulators and customers ask for. Deliverables: - Security architecture reviews and threat modelling - Identity and access management, secrets and key management - Application security: secure SDLC, dependency and code review - Cloud hardening and network security - Incident response, forensics and remediation - Compliance readiness: SOC 2, GDPR, HIPAA, PCI DSS Typical stack: Cloudflare, AWS IAM, GCP IAM, Vault, OWASP tooling, SIEM and logging Q: How do you secure AI systems specifically? A: AI adds new attack surfaces: prompt injection, data poisoning, over-privileged agents. We scope each agent's permissions, validate inputs and tool calls, log every action, and keep sensitive data out of prompts unless the design requires it and the model terms allow it. Q: Do you help with compliance like SOC 2 or HIPAA? A: We build systems so the controls those frameworks require are already in place, and we prepare the technical evidence. Formal certification is done by an auditor; we work alongside them. ### Growth & Marketing Systems URL: https://automaark.com/services/growth-systems Growth is a system too. We build the infrastructure behind acquisition and retention: CRM architecture, outbound engines, funnels, lifecycle automation and the data that connects every touch to revenue. Because we also build the product and the data layer, growth systems at Automaark are wired into the same foundation rather than bolted on with a dozen disconnected tools. Deliverables: - CRM architecture and implementation (GoHighLevel, HubSpot) - Outbound and lead-generation engines with deliverability engineering - Funnels, landing pages and conversion instrumentation - Lifecycle and retention automation - Attribution and revenue reporting - AI-assisted content and personalisation systems Typical stack: GoHighLevel, HubSpot, Smartlead, Segment, Google Analytics, Ads APIs Q: Is this marketing or engineering? A: Engineering applied to marketing. We do not run ad creative; we build the systems that make acquisition measurable, repeatable and connected to the product and the data warehouse. ## How an engagement runs 01. Scope (Before any build): A scoping call, then a written scope with milestones and acceptance criteria. If we are not the right team, we say so. 02. Architect (Before the first sprint): Data model, APIs, auth, security boundaries, deployment topology and the cost curve, recorded as decisions you keep. 03. Build (In milestones): Working software at every milestone, in your repositories and cloud accounts, with tests and documentation as we go. 04. Operate (Ongoing): Monitoring, runbooks and on-call. We run it, or we train your team to, and we stay accountable either way. ## Principles - Architecture first: Data model, APIs, auth, security boundaries and deployment are designed before the first sprint starts. - API-first by default: Every interface is a consumer of the API, so web, mobile and partners share one backend. - Documented as we build: PRDs, decision records, tests and handover docs are part of the delivery, not an afterthought. - Built to be operated: Monitoring, environments and runbooks ship with the system. We stay accountable after launch. - You own everything: Code in your repos, infrastructure in your accounts, credentials in your hands. No lock-in to us. - Honest scoping: A written scope with milestones and acceptance criteria before any build work, and a written answer when the answer is no. ## Industries Hospitality & travel, Real estate & PropTech, Financial services & compliance, Healthcare operations, Retail & e-commerce, Legal technology, Marketing & agencies, Logistics & operations, Professional services ## Core stack TypeScript, Next.js, React, Node.js, Python, Go, PostgreSQL, Supabase, AWS, Google Cloud, Railway, Cloudflare, Terraform, Docker, n8n, GoHighLevel, OpenAI, Anthropic, pgvector ## Frequently asked questions Q: What does Automaark do? A: Automaark is a systems architecture and engineering firm. We design, build and operate production-grade software, AI and agentic systems, data platforms, cloud infrastructure, cybersecurity and growth systems. Clients engage us as the engineering team for a product or platform, or for a specific layer of it. Q: Do you only build AI systems? A: No. AI is one of seven departments. Most of our work is core infrastructure: cloud platforms, backend services and background job systems, marketplaces and booking platforms, data pipelines, security and integration. AI sits on top of that foundation where it earns its place; it is not the reason the foundation exists. Q: What kind of platforms have you built? A: Production platforms of the kind companies run a business on: two-sided marketplaces and booking systems in hospitality and real estate, background verification and compliance SaaS, operational platforms for healthcare and retail, marketing and growth infrastructure, and the cloud, data and security layers underneath them. Case studies are shared on request, some under NDA. Q: Can you take over cloud infrastructure or a backend built by another team? A: Yes. We start with an audit of the code, the cloud environment, access, backups, security posture and spend, then stabilise before changing anything. You get a written assessment first, and documentation and runbooks as part of the handover. Q: What makes Automaark different from an AI development agency? A: Most agencies build features. Automaark designs systems: the data model, the security boundaries, the integration layer and the cost curve come before the code. We use AI heavily in our own work and in what we ship, but what we sell is architecture, which is the part AI tools cannot generate for you. Q: Where is Automaark based? A: Automaark is headquartered in New York. The firm has African roots and a distributed engineering team across North America, Europe, Africa and Asia. Most of our client work is in the United States, Canada and Australia, and we work with clients worldwide. Q: Who founded Automaark? A: Automaark was founded by Olamide Dada, who leads the company as Founder and CEO. Q: What kind of companies does Automaark work with? A: Founders building a SaaS product or marketplace, operators replacing manual processes with automation, and established companies that need cloud, data, security or AI infrastructure running in production. We have delivered systems in hospitality and travel, real estate and PropTech, financial services and compliance, healthcare operations, retail, legal technology and marketing. Q: What technologies does Automaark work with? A: TypeScript, Next.js, React and Node.js on the product side; Python and Go for services and AI; PostgreSQL, Supabase and modern warehouses for data; AWS, Google Cloud, Railway and Cloudflare for infrastructure; OpenAI, Anthropic and open-weight models for AI; n8n and GoHighLevel for automation and growth. We choose tools per project rather than by habit. Q: How much does a project with Automaark cost? A: It depends on scope, and we will not quote before a scoping call. As a guide from published industry ranges in 2026: a well-scoped AI integration typically starts in the low tens of thousands of dollars; a production SaaS platform or multi-system agentic build runs from the high tens into the hundreds of thousands; ongoing operation is usually 15 to 25 percent of build cost per year. Every Automaark engagement starts with a written scope and fixed milestones, so the number is known before work begins. Q: How long does a project take? A: As long as the scope needs, and no longer. We do not quote a duration before scoping, because a number given before the architecture is understood is a guess. What we do commit to is the structure: scope and architecture first, then delivery in milestones with working software at each one, so you can see progress and change direction without waiting for a big-bang launch. Q: How does an engagement start? A: Send a short note through the contact form describing what you are building and where it stands. We follow up with a scoping call, then turn the outcome into a written scope with milestones and acceptance criteria before any build work begins. Q: Who owns the code, data and infrastructure? A: You do. Everything is built in your repositories and cloud accounts, with documentation and runbooks handed over, so you are never dependent on Automaark to run what we built. Q: Do you provide support after launch? A: Yes. Every system ships with monitoring and runbooks, and we offer ongoing operation, improvement and on-call arrangements. We also train your team to run it themselves if that is the goal. Q: How do you handle data privacy and security? A: Security is designed into the architecture: identity and access control, secrets management, network boundaries, encrypted data and audit logs. For AI, data stays in your environment and we use no-training API terms or self-hosted models when required. We support SOC 2, GDPR and HIPAA readiness. Q: Can you work with our existing team and tools? A: Yes. We integrate with existing codebases, clouds, CRMs, ERPs and data warehouses, and we work alongside in-house teams. We start with an audit so you get a written picture of what exists before anything changes. Q: Where is the Automaark team, and does a distributed team affect quality? A: Automaark is headquartered in New York with senior engineers across North America, Europe, Africa and Asia. Every engagement has a named lead in your time zone, one architectural standard, and the same ownership, documentation and accountability wherever the engineer sits. The distributed model is also why we can fund the architecture and operations work that a single-city team usually prices out.