The Engineers Who
Actually Ship.
The same engineers who built MSPPro, SyncroFlow, and Leadera — available as your dedicated startup squad or embedded enterprise specialists. Zero ramp time. Vetted, agentic, ship-ready.
Ship in 48 hours.
Not 48 days.
Squick is our pre-built, cohesive 3-person engineering squad — Tech Lead, Full-Stack Dev, and SRE — already aligned on modern AI-native stacks. No interviews to coordinate, no onboarding drag. They arrive knowing each other's strengths and start shipping Week 1.
Architects the system, sets the technical standard, and unblocks the team. Hands-on from Day 1 — no ivory-tower TLs.
- System design
- Code review
- AI/agent architecture
- Stakeholder comms
Builds the product front-to-back. React, Node, Python, AI integrations — whatever the stack demands, they deliver clean, tested code fast.
- React + Node.js
- LLM integrations
- REST / GraphQL
- TypeScript
Owns infra, CI/CD, and uptime. Provisions GPU instances, manages Kubernetes, and ensures your AI platform stays up when it matters.
- AWS / GCP / Azure
- Kubernetes + Terraform
- CI/CD pipelines
- LLMOps & monitoring
48-hour activation
Squad is briefed, aligned, and in your Slack within 2 business days of signing.
Weekly delivery milestones
Every Friday: what shipped, what's next, what's blocked. Full transparency.
Pre-vetted chemistry
These squads have worked together before. No team-forming lag, straight to norming.
From $15k/mo
One flat rate for the full 3-person squad. No per-seat licensing surprises.
Precision-fit engineers
for enterprise teams.
Fill critical AI and infrastructure gaps with senior specialists who embed directly into your org. They follow your security protocols, integrate with your existing CI/CD, and work inside your teams — not alongside them.
Senior LLM engineers who have shipped production AI — RAG pipelines, agentic workflows, model fine-tuning. Not prompt engineers; systems engineers.
- OpenAI, Anthropic, open-source LLMs
- LangChain, LlamaIndex, DSPy
- Vector search & retrieval systems
- Multi-agent orchestration
Infrastructure experts who build and own the plumbing that keeps AI systems reliable at scale. Kubernetes, Terraform, GPU clusters, and LLMOps.
- Multi-cloud architecture (AWS/GCP/Azure)
- Kubernetes & container orchestration
- GPU provisioning & model serving
- Observability & incident response
End-to-end ML engineers owning the full model lifecycle — data pipelines through production APIs, with monitoring and retraining baked in.
- PyTorch, TensorFlow, JAX
- MLOps pipelines & experiment tracking
- Model fine-tuning & RLHF
- Feature stores & data engineering
Specialists who ensure your AI systems meet SOC2, HIPAA, FedRAMP, and EU AI Act requirements — governance that doesn't slow down delivery.
- SOC2, HIPAA, FedRAMP alignment
- AI bias auditing & explainability
- Data privacy & retention policies
- Model risk management frameworks
Built for enterprise environments
Compliance-ready onboarding
Background checks, NDAs, security clearance docs ready on day one.
Embeds in your org
Works inside your Jira, GitHub Enterprise, Confluence, and Slack.
Senior-only placements
Every specialist has 5+ years production experience. No juniors, ever.
We deploy AI workflows
into your business.
An embedded AI deployment team that maps, builds, deploys, maintains, and improves workflows inside real businesses — through monthly implementation sprints, without you hiring full-time engineers.
AI tools are easy. AI deployment is hard.
We embed close to your operating team — not a generic automation agency shipping demos, but engineers who own adoption, monitoring, and iteration.
Manual workflows everywhere
Email, spreadsheets, approvals, and handoffs keep piling up around the team.
Disconnected tools
The CRM, inbox, docs, Slack, and reporting stack do not share enough context.
No implementation time
Your staff know the pain points, but they do not have weeks to build internal systems.
Hiring is risky
A full-time AI engineer is expensive before the first workflow has proved ROI.
Agencies stop at demos
Generic automation work rarely handles adoption, monitoring, and iteration.
A monthly implementation rhythm,
from bottleneck to adopted workflow.
Workflow systems your team will actually use.
Client onboarding automation
Turn intake forms, kickoff emails, and task setup into one controlled workflow.
Document collection
Chase missing files, summarize gaps, and route exceptions to the right person.
Email triage
Classify inbound requests, draft replies, and protect sensitive approvals.
Reporting automation
Compile client updates and KPI snapshots from scattered systems.
CRM updates
Keep pipeline data current without asking teams to become data clerks.
Internal knowledge base
Make SOPs, decisions, and policies queryable inside everyday tools.
Codex and Cursor rollout
Deploy AI coding workflows with repo rules, security guardrails, and team habits.
Workflow dashboards
Track deployment progress, human review points, and operational outcomes.
Start with a sprint. Scale into a pod.
Your First AI Operator
One production AI workflow built into your existing tools and live in 14 days.
- One workflow
- Production deployment
- Human approval controls
- Documentation
- 14-day launch support
Monthly AI Pod
Embedded deployment capacity for teams that want AI workflows shipped every month.
- Monthly sprint cadence
- Active workflow maintenance
- Integrations
- Team enablement
- Outcome reporting
Custom Deployment
For multi-workflow rollouts, internal platforms, migrations, or complex integrations.
- Workflow architecture
- Custom internal tools
- Data preparation
- Multi-system integrations
- Governance
Tell our AI what you need.
Get a squad brief.
Skip the forms. Our AI Talent Scoper asks the right technical questions — stack, roles, timeline, budget — and compiles a structured brief your team can act on immediately. Takes 3 minutes, saves hours.
Before we begin
Tell us who you are so we can tailor your session.
Open Roles
Work on platforms that are actually in production. Agenticom-certified means you've shipped, not just studied.