Enterprise AI Engineering
Custom AI Agents. Built for Your Business. Deployed for Results.
We build intelligent AI solutions that handle real work—from quotations and order processing to enterprise knowledge, customer service, and complex operational workflows.
- Built around your workflows
- Connected to your existing systems
- Designed for measurable outcomes
- Customer RFQ PDFRFQ-20418 · 14 line items
- Document Understanding AgentSpecs, quantities, due date extracted
- Product Matching Agent13 matched · 1 needs review
- ERP Pricing & InventoryPrice list B · stock confirmed
- Quotation ReviewSales rep approval
- Customer Quote GeneratedQ-88213 sent
Sample dataHuman checkpoint
See what parallel agents do to throughput.
The ScaleXM Multiplier Core
Bottleneck
Agent pipeline
The business problem
Your best people shouldn't be stuck doing repetitive work.
Across growing businesses, valuable time is lost to manual data entry, disconnected systems, repetitive decisions, document handling, and slow customer response.
Manual workflows consume skilled employee hours
Experienced people spend their day re-keying, checking and chasing.
Information is scattered across documents and systems
Answers live in PDFs, inboxes, ERP screens and people's memory.
Slow processes delay revenue and customer service
Quotes, orders and replies wait in queues while customers wait too.
Existing software doesn't handle every business exception
The edge cases that make your business different fall back to people.
We build AI agents that work across your systems to get the job done—with your people making the calls that need judgment.
What we build
AI solutions built around how your business operates.
Use case explorer
Find where AI can make the biggest difference.
Manufacturing · Sales
Common bottlenecks
- Slow quote turnaround
- Manual product lookups
- Follow-ups that slip
- Engineering time spent on routine quotes
Relevant AI use cases
- RFQ processing
- Technical product matching
- Pricing automation
- Quote follow-up
- CRM integration
Data & systems involved
- CRM
- ERP pricing
- Product catalog
Implementation considerations
- Data quality and format variety
- Which steps need human approval
- Integration access to CRM
KPIs worth measuring
- Quote turnaround
- Win rate
- Quotes per rep
Suggested starting point
A quotation agent on your highest-volume product line
Enterprise integrations
AI that works with the systems you already use.
AI only delivers its full value when it can securely connect to the systems where your business operates. Systems shown are examples of what we can connect to.
Data sources
ScaleXM
Custom AI Agents
⇄ bidirectional
Business systems
PDFs & documents: RFQs, invoices, contracts and specs read with layout awareness.
- API-based integration
- Permission-aware access
- Human approval workflows
- Audit trails
- Structured data validation
- Exception management
Industry expertise
Deep business understanding. Serious AI engineering.
Manufacturing
Engineered products, complex quotes and tight schedules. We build AI that understands specs, plans and plant-floor reality.
- RFQ automation
- Production planning
- Technical knowledge agents
- Quality documentation
- Inventory forecasting
Business outcomes
Technology matters. Business results matter more.
Industrial Quotation Automation
An industrial equipment distributor receives RFQs in every format, and estimators re-key each one into the client's ERP system.
Before
- Manual reading of RFQs
- Catalog searches by hand
- Prices copied from ERP
After
- RFQ parsed on arrival
- Matches proposed with confidence
- Pricing pulled automatically
Systems · Email, Client ERP system, Product catalog, CRM
KPIs · Quote turnaround, Quotes per estimator, Match accuracy
Read the storyOrder Processing Automation
Customer service reps spend most of the day typing purchase orders from PDFs and emails into the ERP.
Before
- PO opened and read
- Lines keyed manually
- Errors caught after shipping
After
- Orders captured automatically
- Validated before entry
- Only exceptions need a person
Systems · Shared inbox, ERP, Inventory, Customer master
KPIs · Order entry time, Accuracy, Orders per rep
Read the storyEnterprise Knowledge Assistant
Technicians and service staff lose time finding the right manual, procedure or past fix.
Before
- Search across drives
- Ask senior staff
- Wait for answers
After
- Ask in plain language
- Answer with citations
- Gaps reported to owners
Systems · SharePoint, Ticketing system, Microsoft Teams
KPIs · Time to answer, First-contact resolution, Onboarding speed
Read the storyOur approach
From business problem to working AI solution.
We start with the problem, define measurable success, and build AI around the systems your team already uses.
Understand business operations, workflows, constraints and opportunities.
What happens · Interviews, process walk-throughs and system inventory with your team.
Timelines are set per project once scope, data and integrations are understood.
Commercial approach
Flexible engagements. Accountability for outcomes.
We believe customers should see a clear path to value before making a major AI investment.
AI Discovery & Roadmap
For companies identifying their highest-value AI opportunities.
- Business process assessment
- Use case prioritization
- Feasibility analysis
- Preliminary ROI model
- Implementation roadmap
Focused Proof of Concept
For companies ready to validate a specific AI workflow.
- Defined scope
- Representative data
- Working prototype
- Success criteria
- Results review
Production AI Engineering
For companies ready to implement and scale a solution.
- Full application development
- Enterprise integration
- Security and testing
- Deployment
- Monitoring and improvement
Pricing aligned with real results.
Depending on the engagement, we can explore milestone-based pricing, POC-first engagements, and commercial terms tied to clearly defined acceptance criteria.
Technology & security
Built for enterprise environments.
Multi-model AI architecture
OpenAI, Anthropic Claude, Google Gemini or open-source models, chosen per task.
Enterprise-grade access controls
Role-based access that inherits your existing permissions.
Private cloud & on-premise options
Deploy where your security and compliance needs require.
Reliable integrations
APIs, SQL and connectors with validation and retries.
Monitoring & evaluations
Quality measured continuously against your real cases.
Human-in-the-loop controls
Approvals wherever an error would be costly.
Technology options · OpenAI · Anthropic Claude · Google Gemini · Open-source models · Python · APIs · SQL · Vector databases · Cloud infrastructure
Choose a deployment model
Model access
Private endpoints to approved model providers inside your cloud account.
Permissions
Agents use service identities scoped by your IT team.
Logging
Logs stay in your cloud monitoring stack.
Data flow
Data never leaves your network boundary except to approved model endpoints.
Why ScaleXM
Business expertise meets AI engineering.
We understand business operations
Strong experience in manufacturing, supply chain, enterprise systems and operational processes.
We build custom solutions
Designed around your real workflows rather than forcing generic software onto unique requirements.
We integrate with existing systems
ERP, CRM, databases, documents, APIs and business applications.
We focus on outcomes
Measurable improvements are defined before implementation begins.
We offer flexible deployment
Architecture designed around your security, infrastructure and operational needs.
We work collaboratively
You're involved in requirements, validation, testing and ongoing improvement.
AI readiness check
Where should you start?
Four quick questions point you to the right first step.
Question 1 of 4
Do you have a workflow that repeats daily or weekly?
Insights
Practical perspectives on enterprise AI.
Sales
How AI Agents Automate Industrial RFQ Processing
What it takes to go from a messy RFQ PDF to a review-ready quote.
Integration
Building AI Agents That Work With ERP Systems
Read, validate and write back to your ERP without breaking what already works.
Strategy
Why Enterprise AI Projects Fail After the POC
The gap between a good demo and a system people rely on.
Engineering
RAG vs Fine-Tuning for Enterprise Knowledge
Which approach fits your documents, update cadence and security needs.
What would you automate if your team had more capacity?
Tell us where your business is losing time, dealing with repetitive work, or struggling with disconnected information. We'll help you explore what's possible.