AI infrastructure for startups and enterprise

Build AI your team can operate.

CognicellAI designs and builds the operating layer behind production AI workflows: secure execution, durable state, internal integrations, observability, and governance that let teams ship faster without losing control.

Proof point Built internal AI infrastructure that reduced complex reporting from multiple weeks to under 1 hour.

Schedule a Strategy Call View Cognition

Build safely Sandboxed tools and code

Deploy reliably Stateful workflows and runbooks

Govern clearly Telemetry, audit, approvals

Infrastructure-first AI consulting. For small teams that need speed, and larger organizations that need security, governance, and scale.

Who We Help

Different pressures. Same production gap.

Startups and enterprises both want AI that performs real work. The infrastructure challenge changes with scale, but the foundation is the same: safe execution, controlled access, observable decisions, and workflows your team can operate.

Small to mid-size startups

Launch credible AI capabilities without overbuilding the platform team.

Move from demo to customer-facing workflow with a lean architecture, practical guardrails, and the deployment path you can afford today.

AI feature architecture and implementation support
Agent workflow MVPs connected to real product data
Cloud-native foundations that can grow with traction
Cost-aware model, inference, and infrastructure choices

Outcome: ship an AI feature customers can trust before hiring a full platform team.

Enterprise engineering teams

Operationalize AI across internal systems with governance from day one.

Build agent platforms that integrate with proprietary APIs, respect security boundaries, produce audit evidence, and fit existing cloud and compliance standards.

Internal AI platform and agent infrastructure design
Secure integrations with APIs, data stores, and workflows
Observability, audit trails, approval gates, and controls
AWS, Kubernetes, Terraform, SOC 2, ISO 27001 alignment

Outcome: deploy internal AI workflows with security, auditability, and operational control from day one.

Services

Practical engagements with a clear exit at every step.

Start with an assessment, prove value on real workflows, then expand only when the architecture and business case hold up.

01 Diagnose

AI Infrastructure Assessment

Map your current AI experiments, data access, execution paths, security boundaries, and operational gaps. Leave with a prioritized roadmap, risk map, and implementation estimate.

02 Pilot

Production Agent Sprint

Take one high-value AI workflow from prototype to production candidate with sandboxed execution, durable state, telemetry, model routing, and deployment automation.

03 Harden

Enterprise Agent Platform Buildout

Design and implement the shared operating layer for internal AI systems: identity, permissions, API integrations, auditability, observability, and platform standards.

04 Scale

AI SDLC and Coding Agent Hardening

Help teams using Codex, Claude Code, Cursor, Copilot, or custom agents turn AI-assisted development into a governed workflow with review, CI, and branch intent built in.

Operating Layer

Build, deploy, monitor, and govern production AI workflows.

CognicellAI focuses on the platform capabilities that turn individual AI experiments into systems the business can trust.

Secure Execution

Containerized or isolated runtimes for code, tools, agents, and internal automation.

Workflow Orchestration

Durable sessions, replayable runs, artifacts, queues, and long-running work.

Enterprise Integration

Controlled access to proprietary APIs, data stores, ticketing systems, and product workflows.

AgentOps Observability

Logs, traces, metrics, evals, costs, audit trails, and reviewable operational evidence.

Cloud Architecture

AWS, Kubernetes, Terraform, GitOps, disaster recovery, and cost-aware deployment.

Governance

Reference architectures, approval gates, compliance posture, and production standards.

Use Cases

AI workflows worth operationalizing.

CognicellAI is strongest where AI workflows need to touch real systems, follow policy, produce evidence, and keep working after the demo ends.

Internal research and reporting agents

Connect proprietary datasets, investigative workflows, document generation, and review loops so teams can produce reliable reports in hours instead of weeks.

Secure customer and employee copilots

Build assistants that respect permissions, retrieve trusted knowledge, call approved tools, and leave a reviewable trail for support, operations, and internal teams.

AI coding and SDLC governance

Turn AI-assisted development into an accountable workflow with branch intent, CI gates, review expectations, test strategy, and safe tool execution.

Agent monitoring and command centers

Give engineering and business owners visibility into agent runs, cost, failures, approvals, audit events, and the decisions that need human attention.

RAG and knowledge systems

Design retrieval systems that go beyond chat: data ingestion, access boundaries, evaluation, citations, freshness, and production observability.

DevOps and incident-response agents

Create controlled automation for runbook execution, environment checks, deploy support, triage, and post-incident evidence gathering.

Engagement Model

Start small. Prove the workflow. Scale what works.

Assess

Identify the workflows, risks, integrations, and infrastructure gaps worth solving first.

Pilot

Build one production-shaped workflow against real systems, with a measurable baseline.

Harden

Add controls, observability, deployment automation, runbooks, and security review.

Expand

Standardize the platform and extend proven patterns across teams or business units.

Why CognicellAI

Infrastructure-first, not prompt-first.

Models matter, but production value comes from the system around them: where agents run, what they can access, how work is reviewed, and whether your team can operate the result under real constraints.

Startup speed, enterprise controls

Lean enough for early teams, rigorous enough for security, compliance, and platform owners.

Principal engineering judgment

Cloud, Kubernetes, DevOps, FinOps, observability, and AI platform work from the same seat.

Open-source proof

Cognition shows the architecture in code: agent state, execution, provider config, and platform patterns.

Built around operations

The goal is not a flashy demo. The goal is a workflow your engineers can monitor, debug, and improve.

Proof

Principal-level platform engineering for production AI systems.

Founder Herman Haggerty has led cloud, Kubernetes, DevOps, compliance, FinOps, and internal AI platform work in high-growth environments.

Weeks to under 1 hour Internal AI platform impact on complex investigative reporting.

$125K+ monthly spend recovered Infrastructure optimization and FinOps leadership.

MIT open source Cognition agent harness as technical proof of the operating model.

Next step

Start with an AI Infrastructure Assessment.

In 30 minutes, we will identify the AI workflow with the clearest path to production, the infrastructure risks around it, and the smallest useful next step.

What we will cover

Where AI workflows or copilots can create measurable value
What systems, data, and permissions they need
What must be sandboxed, logged, reviewed, or approved
The right next step: assessment, sprint, hardening, or platform buildout

You leave with a recommended first workflow, a risk map, and a practical next-step plan.

Schedule a 30-minute Strategy Call