Scorpio
Enterprise agentic procurement
Spend intelligence, supplier data and contract compliance across federated ERP estates.
Explore ScorpioAI Product Engineering
We built two enterprise-grade agentic systems.
We can build yours.
What we build
What can we build for you?
AI Agents
Customer, employee and operational agents that understand enterprise context, use tools and take action.
- AI agents
- Voice agents
- Enterprise copilots
Agentic Workflows
Turn multi-step business processes into intelligent workflows that reason, act and involve people where judgment matters.
- Process automation
- Human-in-the-loop
- Multi-agent workflows
AI Products
Build new AI-native products, or embed intelligence into the software, platforms and experiences your customers already use.
- AI product development
- GenAI applications
- SaaS products
Legacy Modernization
Use AI-assisted engineering to understand, re-architect and rebuild critical legacy applications while preserving the business logic they contain.
- Application modernization
- Claude Code
- AI code migration
POC to production
Getting to production is the hard part.
A demo can take weeks. Building an AI system your enterprise can depend on is harder.
- Context
- Evals
- Security
- Agent orchestration
- Token economics
- Model independence
We solved these problems building Scorpio and Auron. Now we bring that engineering experience to yours.
Built twice already
We built ours before offering to build yours.
Auron
Enterprise agentic customer engagement and selling
Voice and chat agents that engage, qualify and sell, grounded in the enterprise systems of record.
Visit useauron.aiWhat building them required
Building and operating these systems meant solving the same production problems our customers meet: context, data, orchestration, evaluation, security, integrations, observability, token economics and model selection.
Enterprises running on systems this team built
- 95%
- Spend classification accuracy at a global food ingredients manufacturer, up from 70%.
- 5 weeks to 5 days
- Enrichment lead time at a global consumer goods company, with 90%+ less manual effort.
- 6%+
- Procurement savings at a global life sciences company, across federated SAP systems.
- 3 days to 3 hours
- Invoice processing time at a leading diamond and jewellery manufacturer.
2-2-2 Methodology
Two hours. Two days. Two weeks.
An iterative way to align on the use case, and to fail fast if it is the wrong one.
- 2 Hours
Kickoff
Understand the problem, outcome and stakeholders.
- 2 Days
Workshop
Map the workflow, users, data, architecture, security and success criteria.
- 2 Weeks
POC
Build enough to prove the use case, the architecture and the path to production.
What you can answer afterwards
Is this the right use case?
What should we buy versus build?
What should we own?
What common architecture and controls will we need?
What is the path from POC to production?
How we work
From use case to production.
Discover
Where agentic AI pays.
Architect
The system, and the context layer beneath it.
Build
Integrations, agents, workflows.
Evaluate
Evals engineered in, not bolted on.
Secure
Identity, access, policy, audit.
Deploy
Into production, on infrastructure you trust.
Operate
Performance and token economics at scale.
The objective is not another AI pilot.
It is a system your enterprise can trust, operate and scale.
AI engineering capabilities
AI engineering expertise from model to production.
Agentic AI development, LLM engineering and the platform work underneath: the disciplines it takes to move an enterprise AI system from prototype to production.
Agentic AI Engineering
- AI agents
- Multi-agent systems
- Agent orchestration
- Tool use
- Human-in-the-loop
- Loop engineering
Context & Data Engineering
- Context engineering
- RAG
- Knowledge graphs
- Embeddings
- Agent memory
- Data pipelines
Evals & Observability
- LLM evals
- Agent evals
- Golden datasets
- Regression testing
- Tracing
- LLMOps
Model Engineering
- Model selection
- Model routing
- Fine-tuning
- Open-weight models
- Token optimization
Prompt & Loop Engineering
- Prompt engineering
- System prompts
- Structured outputs
- Agentic loops
- Self-correction
AI Security & Governance
- Guardrails
- Agent identity
- Least privilege
- Prompt-injection defense
- Policy enforcement
- Audit
Enterprise Integration
- MCP
- APIs
- ERP integration
- CRM integration
- Tool registries
- Enterprise connectors
Forward-Deployed Engineering
- Embedded AI engineers
- Architecture
- Rapid prototyping
- Production engineering
Emerging practice
- Loop engineering
- Loop engineering is the design of iterative agent workflows in which the agent acts, observes the result, decides the next step and repeats, pursuing a goal across many turns rather than depending on a person to re-prompt it at each step.
Partnerships
Building with the companies defining AI.
Anthropic
Claude Partner Network
An Anthropic partner, with production systems built on Claude. Anthropic models run inside Scorpio and Auron today, and both were built with Claude Code.
A differentiated service
Modernize critical software with Claude Code
Generating new code is becoming easier. Understanding what the old system actually does, and proving the new system preserves what matters, is still engineering.
Six stages, end to end
Assess
Read the estate as it is: what runs, what is dead, what nobody dares touch.
Map
Dependency graphs and data flows across modules documented once, years ago.
Extract Rules
Recover the business logic buried in the code, most of it written down nowhere. This is where a rebuild is won or lost.
Reimagine
Design the target: what becomes a service, what becomes an agent, what stays strictly deterministic.
Transform
Rebuild, with equivalence tests proving the new system behaves as the old one did where it matters.
Harden
Wrap it in the controls that make a business willing to cut over.
Who does what
Claude Code accelerates understanding and transformation across a codebase larger than any team could read. Oraczen engineers supply the architecture, the business-rule recovery, the equivalence testing, the security and the production hardening.
Where it runs
Claude Code runs inside your own estate: Amazon Bedrock in your VPC with private endpoints, Google Vertex, Microsoft Foundry, or your own keys. Your source never has to leave.
Hardened with reusable capabilities
- Identity
- Policy
- Evaluation
- Observability
- Audit
Built once as shared services, so every rebuilt application and every agent inherits them instead of reinventing them. Models and tools accelerate the rebuild; enterprise engineering decides whether the organization can safely cut over.
On why that control layer decides the outcome, see Why The Harness Matters More Than The Model — YC Paper Club
Adjacent, and already in production: standardizing a fragmented ERP estate into a queryable foundation, then putting agents on top of it.
Real-time procurement intelligence and field capture for EPC contractorsAn EPC contractor
Our point of view
How we design the control layer.
Harness design, and where the boundary between buying and owning should sit.
Anatomy of an agent, and the dimension the market under-serves
A reference architecture, and which layer has to stay yours
Buy agents without buying their architecture
A twelve-statement readiness checklist
PDF · 6 pages · sent to your inbox
AWS
Cloud infrastructure
Systems built on AWS, with Bedrock and the surrounding services as the runtime. We do not rebuild the substrate; the differentiated engineering sits above it.
Sovereignty
Own your AI sovereignty.
Your models will change. Your enterprise intelligence should remain yours.
Own your data
Enterprise data stays governed on your terms.
Own your context
Your knowledge, memory and business context remain enterprise assets.
Stay model independent
Use the best proprietary models today, and open models where they offer better control, economics or deployment flexibility.
Own the control layer
Identity, access, orchestration, evaluation and audit remain independent of any single model or provider.
Use the best models. Own the intelligence. Control the future.
Proof and trust
Built globally. Engineered for the enterprise.
United States · United Kingdom · India
Customer-facing and forward-deployed engineering in the US and UK, backed by deep product and AI engineering capability in India.
Certified, partnered and recognized
SOC 2 Type II
Independently audited security, availability and confidentiality controls, with an on-premise option where data cannot leave your estate.
- AnthropicClaude Partner Network
- AWSCloud partner
Recognition
Industries
Where this engineering is already deployed.
Financial Services
Regulated agentic systems where entitlements and a complete decision trace are requirements.
Manufacturing
Spend intelligence and supplier data across federated ERP estates.
Supply Chain & Procurement
Sourcing, contract compliance and supplier risk, agent-operated.
B2B Sales
Engagement and selling agents grounded in the systems of record.
Media & Entertainment
Production and distribution workflows on agentic infrastructure.
Enterprise Software
Contract and supplier governance across large subcontractor networks.
In production, at enterprise scale.
Engineering, Procurement & Construction (EPC)
AI Buildcon finally knows what it's paying its subcontractors for
AI Buildcon runs multiple active job sites as an EPC general contractor, high subcontractor cost pressure, hard schedule risk, and procurement data that had never once been asked to answer a question.
Read the story
Procurement, Supply Chain
Clearing the Enterprise data fog with AI-driven classifications and Agentic spend analysis
A diversified enterprise specializing in food products has a decades-long legacy of driving innovation across agriculture, food technology, and industrial applications. With a global manufacturing and distribution footprint and a workforce of over 10,000, the company partners with enterprises across consumer goods, health sciences, and industrial sectors to co-develop high-performance, nature-derived products.
Read the story
Procurement / Finance
Scaling to Millions of Transactions with +90% Spend Classification Accuracy Using Agentic Systems
A Fortune Global 500 manufacturer with $15 billion in annual revenue faced a persistent procurement challenge: millions of annual transactions had to be classified across multiple taxonomies with high accuracy, speed, and compliance. Inconsistent naming, free-text purchase descriptions, and decentralized procurement structures made reliable classification difficult to scale. Manual processes consumed 12,000 hours annually and delivered a 20% misclassification rate. PSA, an agentic system built on Oraczen’s Zen Platform, deployed an AI-driven classification capability combining machine learning, Retrieval-Augmented Generation (RAG), large language models (LLMs), and human-in-the-loop (HITL) oversight. The result: 95% classification accuracy, 85% time savings, and $30 million in identified cost-saving opportunities.
Read the storyQuestions
What enterprises ask us first
You have an AI product to build.
We have built two already. Let's build yours.