Encompix + private AI
AI for Encompix, Grounded in Your Project and Job Cost Data
Short answer
AI on Encompix, the ECI Software Solutions ERP built around project-based engineer-to-order manufacturing, works by connecting to its Microsoft SQL Server database and reporting layer to ground a private language model on your project structures, estimates, engineering change orders, and job cost transactions. For an operations manager running heavy equipment or capital equipment production on Encompix, that means plain-language answers about project margin and schedule risk, on infrastructure the manufacturer controls rather than a public AI service.
- ERP
- Encompix, Encompix ETO ERP
- Industries
- Heavy Equipment, Capital Equipment, Engineer-to-Order Manufacturing, Industrial Machinery
- Written for
- Operations Manager
Encompix organizes a manufacturer's entire business around the project: estimating, engineering, procurement, production, and financials all tie back to a single job or project record, which is exactly the structure a heavy equipment or capital goods manufacturer needs when every unit is effectively its own contract. That project-centric design is a strength for control, but it also means every question an operations manager asks, from margin to schedule risk, ultimately requires pulling and reconciling data across several linked modules.
In practice, that reconciliation work falls on a small group of people who understand how Encompix ties estimating line items to actual labor, material, and subcontract costs on a live project. A monthly project review means someone building a work-in-progress summary by hand, and a change order on one project rarely gets checked automatically against its downstream effect on committed costs and delivery dates.
A private AI layer grounded on Encompix data changes that. An operations manager can ask 'which projects are trending over their estimate and why' and get an answer built from actual project cost transactions and the original estimate, with the underlying records shown so it can be verified rather than trusted blindly. The same grounding supports a project status assistant that pulls real committed costs and schedule data instead of a manually updated tracking spreadsheet.
Encompix is typically run on customer-managed Microsoft SQL Server infrastructure, which gives an operations manager a straightforward data placement decision for the AI layer: the model and its extract can sit on the same network and under the same controls as Encompix itself, so project, customer, and cost data never need to leave infrastructure the manufacturer already trusts.
What usually gets in the way
The problems we hear most from operations manager teams running Encompix.
Project margin visibility lags the actual work
Knowing whether a project is tracking to its estimate means reconciling committed and actual costs across engineering, procurement, and labor, which usually happens on a monthly cycle rather than continuously.
Engineering change orders do not auto-propagate their cost impact
A change order on a live project can shift material, labor, and subcontract cost, but seeing that impact clearly requires someone to manually trace it through the project structure.
Estimating leans on institutional memory
Pricing a new, similar-but-different piece of capital equipment relies on an estimator's recollection of past projects rather than a systematic pull of comparable historical costs.
Work-in-progress reporting is a manual build
WIP summaries for finance and leadership are typically assembled by exporting and reconciling project cost data by hand each reporting period.
Cross-project knowledge does not transfer
A supplier issue or design problem resolved on one project rarely makes it into a form the next project manager can find when a similar issue comes up on a different job.
Where AI earns its place in Encompix
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Project margin and variance explanation
An operations manager asks why a project is trending over budget and the assistant pulls committed and actual cost transactions against the original estimate to explain the gap.
Touches: Encompix Project Cost transactions, Estimates, Engineering records
Outcome: Turns a monthly manual reconciliation into an answer available on demand, not just at period close.
Engineering change order impact summary
When a change order is issued on an active project, the assistant traces its effect on committed material, labor, and subcontract cost and drafts a plain-language summary for review.
Touches: Encompix Engineering Change Orders, Project Cost structure, Purchase Commitments
Outcome: Surfaces cost and schedule impact before it becomes a surprise at the next project review.
Estimating assistant using historical project costs
For a new quote on similar equipment, the assistant retrieves actual costs from comparable past projects by configuration and component to suggest a starting estimate.
Touches: Encompix Estimating module, historical Project Costs, BOM/Routing records
Outcome: Gives estimators a data-grounded starting point for pricing complex, low-volume capital equipment.
Work-in-progress summary drafting
The assistant assembles a current WIP summary across active projects directly from Encompix cost data, drafted in the format finance already uses for reporting.
Touches: Encompix Project Financials, WIP accounts, Billing schedules
Outcome: Cuts the manual build time for WIP reporting from days to a review pass each period.
Natural-language project status queries
A project manager or executive asks 'what is the status of the Unit 14 build' and gets an answer grounded in current engineering, procurement, and production status for that project.
Touches: Encompix Project status fields, Production schedule, open Purchase Orders
Outcome: Reduces status-request interruptions to project managers who already know the data by heart.
Subcontractor and supplier commitment tracking
The assistant flags projects where committed subcontract or purchase costs are approaching or exceeding the budgeted amount, before the invoice arrives.
Touches: Encompix Purchase Commitments, Subcontract records, Project Budgets
Outcome: Gives operations a heads-up on cost overrun risk before it is locked in by a received invoice.
Cross-project issue and resolution search
An engineer describes a design or supplier problem in plain language and the assistant searches prior project records and notes for similar issues and how they were resolved.
Touches: Encompix Project notes, Engineering records, prior change order history
Outcome: Cuts the time spent rediscovering a fix another project team already found.
Reference architecture
The architecture connects to Encompix through its SQL Server database and reporting layer, keeping the model and any extracted data on the same infrastructure controls the manufacturer already applies to Encompix itself.
- 1
Encompix connectors
SQL Server reporting views and, where available, the platform's integration tools for structured extracts of project, estimating, engineering, and cost data.
- 2
Data and semantic layer
Extracted project cost, estimate, and change order data organized into a semantic model that maps Encompix's project-centric structure to the terms operations and finance actually use.
- 3
Model serving
An open-weight model served with vLLM or Ollama on infrastructure sized to the manufacturer, from an on-prem server beside the SQL Server instance to a private cloud tenant.
- 4
Retrieval and agents
Project margin, status, and estimating questions are answered through retrieval grounded in the Encompix extract; any write-back goes through the platform's own interface with a named person approving it.
- 5
Governance and audit
Access mirrors Encompix user permissions by project role, and every question and answer is logged for finance and project leadership to review.
Integration notes for your ERP team
- SQL Server reporting views are the practical path to project, cost, and engineering data; building the semantic layer against a stable view set helps it survive Encompix version updates.
- Encompix's project-centric data model means most joins run through the project or job identifier; encoding that relationship once in the semantic layer keeps every downstream question consistent.
- Change order history should be linked explicitly to the project cost lines it affects, since that link is what makes an impact summary trustworthy rather than a guess.
- Encompix user permissions, typically scoped by project role, should be mirrored into the AI layer's access model so a project manager sees answers grounded only in projects they are assigned to.
- Any write-back, such as a draft WIP adjustment or a flagged commitment overrun, should go through Encompix's own interface with a named person approving it rather than a direct database write.
- Where Encompix integrates with CAD or PLM systems for engineering data, plan the semantic layer to pull configuration context from those linked sources rather than treating Encompix as the sole source of engineering truth.
Deployment options
Air-gapped or on-site, beside on-premise Encompix
Heavy equipment and capital goods manufacturers running Encompix on their own SQL Server infrastructure
The AI model and data extract run on the same on-premise network as Encompix, with no data leaving the building for AI processing.
Private cloud, matched to IT infrastructure strategy
Manufacturers consolidating infrastructure into a private cloud alongside other systems
The extract and model run in a private cloud VPC the manufacturer controls, fed by scheduled or near-real-time pulls from the Encompix database.
Hybrid: plant-level inference, central project rollup
Multi-site manufacturers building similar equipment across more than one plant
Lightweight inference runs near each plant's Encompix instance for project-level questions, while a central private environment aggregates cross-project margin and issue trends for corporate leadership.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
CMMC 2.0 / DFARS 252.204-7012
Manufacturers running Encompix that also hold DoD subcontracts keep controlled unclassified information referenced in project records inside their own private AI environment, never passed to a shared or public model.
ITAR
Where project engineering records reference export-controlled equipment or designs, the retrieval layer respects the same access scoping so the assistant cannot surface that content to an unauthorized user.
SOX or group internal controls
The AI layer never posts directly to project financials; every suggested WIP figure or cost reclassification is reviewed and approved by a named finance user before it enters Encompix or a board pack.
State data breach notification laws
Customer and project data extracted for AI grounding is minimized to what each use case needs and kept on infrastructure the manufacturer controls.
Where Netray fits
ERPray
The grounded question-answering pattern fits project margin and status questions well; an Encompix connector is scoped as part of the engagement since it is not a shipped connector today.
Custom build
WIP summary drafting, change order impact tracing, and estimating assistants are typically bespoke, tuned to the manufacturer's own project structure and equipment families.
How an engagement runs
Phase 1 . 2-3 weeks
Discovery
- -Review of the Encompix SQL Server environment and current reporting practices
- -Priority use case selection with operations and finance leadership
- -Draft semantic model for project, estimating, and cost data
- -Data sensitivity review to confirm on-prem or private cloud placement
Phase 2 . 6-8 weeks
Pilot
- -SQL Server integration for the selected project and cost data
- -Private model deployed in the agreed environment
- -One or two use cases live, e.g. project margin explanation and WIP drafting
- -Access controls mapped to Encompix project roles
Phase 3 . 8-10 weeks
Production
- -Expansion to change order impact and estimating assistance
- -Query and access logging reviewed by finance
- -Training for project managers and estimators
- -Handover runbook for ongoing operation
Phase 4 . Ongoing
Scale
- -Rollout to additional plants running Encompix, where applicable
- -Cross-project trend views for corporate operations leadership
- -Model and prompt updates as equipment families or processes change
- -Capacity planning for infrastructure as usage grows
Questions to ask any vendor, including us
A short list that separates real Encompix AI work from a chatbot demo.
- Where exactly will our project and cost data be stored and processed for AI, given our SQL Server environment?
- Can the assistant show the underlying Encompix records behind a margin or WIP answer, so we can verify it?
- How does the tool's access control map to our existing Encompix project role assignments?
- Does any AI-suggested WIP figure or estimate require a named person's approval before it is used?
- How is the connector kept working as Encompix or its underlying database schema changes?
- What happens to our extracted data and semantic model if we end the engagement?
- How does the vendor account for our CAD or PLM integration when modeling engineering data?
- What is the realistic ongoing infrastructure cost for our plant size?
Frequently asked questions
Can AI be added to Encompix without sending project data to a public AI provider?
Yes. Encompix's SQL Server database and reporting layer support extracting project, cost, and engineering data into infrastructure the manufacturer controls, where a privately hosted model answers questions grounded in that data. No project data needs to reach a public model API for this to work.
Does this work with our on-premise Encompix installation?
Yes. Most Encompix customers run on-premise SQL Server, and the AI layer's model and data extract can run on the same on-premise infrastructure, under the same network controls already applied to Encompix.
Can the assistant post WIP adjustments directly to Encompix?
It can draft a suggested WIP summary or adjustment, but the recommended pattern is that a named finance user reviews and enters it through Encompix's own interface. The assistant should never have unsupervised write access to project financials.
How does this help with engineer-to-order estimating?
The assistant retrieves actual costs from comparable historical projects, matched by configuration and component, to give estimators a data-grounded starting point instead of relying purely on recollection for one-off capital equipment.
Will this replace our current project review process?
No, it complements it. The assistant surfaces margin trends and change order impacts faster so the project review conversation starts from current data instead of a manually assembled summary.
Is this an Encompix or ECI Software Solutions product?
No. This is a separate, privately hosted AI layer built using Encompix's existing database and reporting access, independent of ECI's own product roadmap. It gives a manufacturer AI on their own data today, on infrastructure they control.
How long does an Encompix AI pilot take?
A focused pilot on one or two use cases, such as project margin explanation and WIP summary drafting, typically takes six to eight weeks from kickoff to a working assistant a defined group of project managers is using.
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Talk it through with an engineer who knows Encompix
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.