Production AI for companies that need to own what they build.
Infrastructure your company owns outright.
THREE PILLARS
Production-grade systems owned by your team: built to hold through diligence, audit, and any transition from day one.
01
AI Discovery
A clear picture of what AI should actually do for your environment, before any build begins.
✓
Working AI demonstration, running against your real data
✓
Production-grade blueprint
✓
Component-level mapping of infra and pipelines
✓
Independent documentation the team owns end-to-end
02
Production Development
Infrastructure built to survive due diligence, without platform dependency.
✓
M&A-ready AI roadmap
✓
Private tooling with full data + policy control
✓
Full IP transfer on delivery
03
EMBEDDED AI SYSTEMS
Multi-agent systems that execute real workflows, end-to-end.
✓
Workflow intelligence mapping
✓
Custom multi-agent architecture and orchestration
✓
Integrations with CRMs, ERPs and operational tooling
✓
Continuous monitoring and performance accountability
CODE AS DELIVERABLE
AI that earns its keep, in plain code.
Every agent we ship is auditable. Read the prompts, follow the reasoning, watch the trace. The system your engineers extend is the system the board signs off on.
REASONING AUDITED
TOOLS OBSERVABLE
FULL TRACE, EVERY RUN
agent_run.log
# Blueprint → Build → Deploy
def deploy_agent(client):
print(f"[INFO] Reading repo: {client.repo}")
stack = inspect.environment()
policy = client.governance.load()
# Multi-agent assembly
agent = Algorama.assemble(stack, policy)
metrics = agent.run(real_data=True, sandbox=False)
# Post-deploy verification
assert metrics.uptime >= 99.9
assert metrics.accuracy >= benchmark
assert metrics.audit == "complete"
> [OK] Agent shipped to production · 6 days · 0 incidents
> [OK] IP transferred to client.engineering · SOW closed
> ▮
agent_run.log · v2.4
117 deploys · 0 incidents · p95 880ms
WHO WE WORK WITH
For the people who have to stand behind the system.
01
CTOs and technical leads
You have shipped production systems before. You know the difference between something that works and something that holds. Algorama embeds in the stack, follows existing engineering standards, and builds AI the team can own, extend, and debug without us.
See case studies
→
02
Founders and operators
AI is moving fast and the board is asking questions. Algorama builds the infrastructure that changes the answers. Owned systems, measurable ROI, and a team accountable for it running.
See case studies
→
03
Research and domain-led teams
Your workflows and data do not fit any off-the-shelf platform. Algorama builds inside your systems, your data architecture, and your regulatory environment. The team keeps working the way it works.
See case studies
→
PROCESS
FEATURED CASE STUDIES
