Introducing the Multiplier Core
SCALExM deploys governed AI agents into the workflows where work stalls — and turns each bottleneck into compounding capacity.
Try it → pick a bottleneck and scale the agents.
Bottleneck
Agent pipeline
Illustrative simulation
Platform
Pre-built infrastructure, orchestration, and security already solved — so deployments take weeks, not quarters.
Intelligent routing across OpenAI, Anthropic, Google Gemini, and open-source models. Zero vendor lock-in.
SOC 2-aligned controls, role-based access, zero unauthorized data retention, and dedicated VPC subnets.
Elastic throughput that grows from a single bottleneck workflow to enterprise-wide operations.
Compose agents into pipelines that run end-to-end work, not single tasks.
Native connectors for ERP, CRM, HRIS and data warehouses. No rip-and-replace.
Role-based policies, human approval gates and full audit trails on every action.
Throughput, cycle time and cost per outcome — reported against your own KPIs.
95%
of enterprise AI projects stall in proof-of-concept. Ours deploy to production in weeks.
Outcomes
Every engagement follows the same arc: find the queue, deploy the pipeline, measure the multiple.
Bottleneck
Customs documentation queues — 1,400 declarations a day keyed manually across three systems, with error-driven rework at every border crossing.
Solution delivered
An autonomous document pipeline: agents extract, validate, and cross-check declarations against tariff rules, escalating only true exceptions to human brokers.
Business outcome
74% zero-touch processing
Clearance cycle time cut from 9 hours to 40 minutes.
Bottleneck
Accounts payable reconciliation — a six-person team matching 9,000 invoices a month against POs and receipts, with month-end close slipping every quarter.
Solution delivered
A matching and exception-resolution agent pipeline wired directly into the ERP, with human approval gates above configurable thresholds.
Business outcome
91% reduction in cycle time
Month-end close moved from day 12 to day 3.
Bottleneck
Compliance and diligence review — analysts spending 60% of each deal cycle reading contracts, filings, and data-room documents by hand.
Solution delivered
A clause-parsing and risk-scoring pipeline that reads every document, flags exposure, and drafts the diligence memo for partner review.
Business outcome
3.2× more deals screened
Diligence turnaround dropped from 6 weeks to 9 days.
Bottleneck
RFP and security questionnaire queues — 200+ competitive bids a quarter, each pulling 40 hours of subject-matter time from product and engineering.
Solution delivered
A retrieval-grounded proposal pipeline that drafts answers from the approved content library, flags unsupported claims, and routes gaps to the right owner for sign-off.
Business outcome
5.8× more bids submitted
Average response turnaround cut from 11 days to 36 hours.
Bottleneck
RFQ intake and bid comparison — 900 supplier quotes a month arriving as PDFs and email threads, scored by hand in spreadsheets by three buyers.
Solution delivered
An RFQ pipeline that normalizes supplier quotes to a common scope, validates them against engineering specs, and returns ranked bid comparisons with negotiation flags.
Business outcome
4.1× more RFQs quoted
Sourcing cycle shortened from 18 days to 4; off-contract spend down 22%.
Bottleneck
Deal desk congestion — non-standard quotes, discount approvals and legal redlines all batching into the final week of every quarter.
Solution delivered
A deal-desk agent that assembles compliant quotes, enforces guardrail pricing rules, and prepares redline summaries for finance and legal approval.
Business outcome
2.4× quote throughput
97% of quotes now issued before quarter close, up from 61%.
Representative engagements. Figures anonymized.
The difference
Generic copilots
Rented by the seat
SCALExM Purpose-Built AI
Owned by you
Method
One workflow. Fixed scope, fixed timeline, production at the end — not another pilot.
Map the constraints, lock scope, and agree the evaluation criteria that define success.
Assemble and fine-tune agent pipelines inside your private environment.
Live user testing on real daily operational tasks, tuned against your team's judgement.
Production deployment with full governance, audit trails, and ownership handover.
Use cases
Start where queues are longest. Expand wherever the multiple holds.
Deploy in your tenant. Models never train on your records.
Approval thresholds you set, enforced on every agent action.
Every decision logged, explainable and exportable.
A 45-minute executive briefing. One workflow. A clear model of what the Multiplier Core would change.
Request a briefing