Multi-plant, multi-entity, multi-currency operations need more than an app. They need research that finds the real constraint, architecture that survives ten years of change, AI you can govern and delivery your board can audit. This page shows exactly how we do each of those.
01 · The problem at your scale
It is a landscape of ERPs, spreadsheets, plant systems and people that grew one decision at a time. We engineer for that landscape — not around it.
Each plant runs its own spreadsheets, codes and habits. Group numbers are stitched together by hand every month.
How we engineer for it: One data model with plant-level configuration, group-level reporting and common masters.
Finance and purchase live in the ERP; the shop floor, quality and maintenance live outside it.
How we engineer for it: Integrate with the ERP as system of record; extend it with production, quality and machine intelligence.
Expensive equipment with PLCs and SCADA, but no live link to planning, cost or maintenance.
How we engineer for it: Read-only OT integration through edge gateways, segmented from your IT network.
Supervisors, contractors and shifts rotate; knowledge leaves with them.
How we engineer for it: Role-based apps, local languages and process knowledge captured as recipes and checklists.
Every number must be traceable — to a batch, a person and a time.
How we engineer for it: Immutable audit trails, versioned masters and lot genealogy built into the data model.
New entities arrive with their own systems and charts of accounts.
How we engineer for it: Multi-company, multi-currency tenancy and a repeatable onboarding playbook for new sites.
02 · How we understand the problem
Research runs in three tracks at the same time — people, process and data — so the solution is shaped by what actually happens on your floor, not by what the org chart says.
Track · People
Output: Personas and stakeholder map
Track · Process
Output: As-is maps and loss trees
Track · Data
Output: Data inventory and quality report
Illustrative walkthrough
Week 1 · Observe
Gate to dispatch over three shifts. Weighbridge, lab, process and packing registers photographed.
Week 1 · Ask
Plant head, shift in-charges, lab, stores and accounts each give a different reason.
Week 2 · Measure
Weighbridge, moisture, production and dispatch joined for one quarter, batch by batch.
Week 2 · Find
Variance traced to intake moisture recorded after weighing, and to one stage without a yield check.
Week 3 · Propose
Moisture at the gate tied to price, stage-wise yield capture and a daily yield report.
You receive:
Client assessment reportAs-is process mapsLoss treesData inventoryStakeholder mapPrioritised opportunity listSuccess measures with baselinesPhased roadmap03 · How we architect
Each data object has exactly one master — ERP, Anvaya or plant system.
What it means for you: No duplicate entry, no month-end reconciliation.
Companies, plants, warehouses and currencies modelled from day one.
What it means for you: Add a plant or acquisition without a re-implementation.
Every capability exposed through versioned, documented APIs.
What it means for you: Your IT team can extend and integrate without us.
Supply chain, production, quality, finance and people as separable modules.
What it means for you: Start small; switch on modules as value is proven.
Least-privilege roles, encryption and audit built in, not configured later.
What it means for you: Pass security reviews without retrofits.
Plant gateways and field apps buffer data when networks drop.
What it means for you: No data loss on the floor or in the field.
Metrics, logs and traces for every service.
What it means for you: Issues found before users report them.
Open formats, exportable data and documented interfaces.
What it means for you: You are never locked in.
Architecture decision record · Example
Context
Field teams work in villages with no network for hours. Visits, maps and onboarding must not wait for connectivity.
Options considered
Decision
Offline-first: local database on device, queued sync with conflict review on the portal.
Consequences
More complex sync logic and testing; in return, zero lost visits and verifiable GPS evidence captured at the moment of work.
04 · Integration with what you already run
ERP
Items, vendors, customers and ledgers from your ERP; production, consumption and quality results back to it.
Typical: SAP, Oracle, Microsoft Dynamics, Tally, others [confirm]
Statutory
Invoices and dispatches flow to statutory portals from the same transaction.
Typical: GST e-invoice, e-way bill [confirm]
Events
Order released, batch closed, machine stopped — published as events other systems can subscribe to.
Typical: Webhooks, message queues
Identity
Users and roles managed in your identity provider.
Typical: SAML / OpenID Connect providers
OT
PLC tags, SCADA alarms, weighbridges and scanners through edge gateways.
Typical: OPC UA, Modbus, MQTT
Data
Curated data feeds into your BI or data platform.
Typical: Scheduled exports, warehouse connectors
Systems we commonly connect to include ERPs, accounting software, CRMs, HR and payroll, e-invoicing and e-way bill portals, weighbridges, PLC/SCADA and identity providers. [Confirm the named systems you have delivered integrations for before publishing.]
05 · AI you can govern
Every AI feature in ASTR is designed around one rule: AI proposes, people approve, the system records. Here is what that means in engineering terms.
Recipes, BOMs, schedules and purchase requests generated by AI wait for a named approver before they take effect.
Answers are built from your own records and documents, with links back to the source rows.
ASTR only reads data the asking user is allowed to see.
Your data is never used to train models for other customers.
AI features are tested on representative cases from your plant before going live.
Every prompt, answer, approval and edit is logged with user and time.
Low-confidence outputs are flagged for review instead of being presented as fact.
AI can be enabled, limited or turned off by module and role.
AI lifecycle
1. Define use case & metric2. Prepare & permission data3. Evaluate on your cases4. Pilot with approvals5. Monitor quality6. Review & retrain06 · Scale, performance & reliability
| Dimension | How we engineer it | Target we agree | How you see it |
|---|---|---|---|
| Availability | Redundant services, health checks, automatic failover | Uptime 95% monthly | Status page and monthly report |
| Response time | Indexed queries, caching, performance budgets per screen | p95 ms for key screens | Release performance report |
| Concurrency | Stateless services that scale horizontally | N concurrent users per tenant | Load-test results before go-live |
| Data volume | Partitioned history, archiving policies | Years of transactions online | Capacity review each quarter |
| Machine data | Time-series storage with downsampling | Signals/sec per plant | Gateway and ingestion dashboards |
| Recovery | Encrypted backups, point-in-time restore, DR runbooks | RPO · RTO | Annual DR test report |
| Offline tolerance | Edge buffering and mobile queued sync | few hours without network | Sync status per device |
| Change safety | Automated tests, staged rollout, instant rollback | Zero-data-loss releases | Release notes and change log |
08 · Newest technologies, applied
| Technology | What it does on your floor | Metric it moves | Where it runs | Maturity |
|---|---|---|---|---|
| Agentic AI with approvals | Agents draft purchase requests, re-plan schedules after a breakdown and prepare reports — then wait for approval. | Planning effort, response time | Cloud | Pilot |
| Generative drawings & plans | Floor plans, layouts and product drawings from a brief, then BOQ and BOM from the drawing. | Estimation time, BOM errors | Cloud | In production |
| Vision AI for drawings | Dimensions, parts and notes read from PDFs, scans and photos. | Take-off time | Cloud | In production |
| Vision AI for quality | Defects detected from line cameras and inspection photos. | Defect escape rate | Edge + cloud | Pilot |
| Predictive maintenance ML | Signal trends compared with OEM limits to flag failures days ahead. | Unplanned downtime, MTBF | Edge + cloud | Pilot |
| Live digital twins | Plants modelled in 3D/2D and bound to PLC signals. | Visibility, response time | Cloud | Pilot |
| Natural-language analytics | Plain-language questions become exact reports and files. | Report turnaround | Cloud | In production |
| Edge AI | Models running on plant gateways for instant, offline decisions. | Latency, uptime | Edge | R&D |
Maturity: In production = live with customers · Pilot = running with selected customers · R&D = in development. [Confirm each status.]
09 · Quality engineering & release
01
Story with acceptance criteria
02
Feature branch, peer review
03
Unit, integration, end-to-end
04
Security and dependencies
05
Your team tests on staging
06
Change approval for production
07
Staged rollout, monitoring
08
Post-release checks, rollback ready
Code, tests, docs and release note — or it isn’t done.
Critical business flows covered end to end.
Every change reviewed by a second engineer.
Development, staging and production fully separated.
Every release documented in plain language.
Key screens measured against agreed targets each release.
Contrast, labels and keyboard support checked.
Architecture, runbooks and admin guides handed over.
10 · The team on your programme
Single point of accountability for outcomes and commercials.
Throughout
Owns architecture, integration design and decision records.
Discovery → go-live
Process maps, requirements and acceptance criteria.
Discovery → UAT
Research, prototypes and usability testing.
Design → build
Full-stack, mobile, data and AI engineers.
Build → hypercare
Machine connectivity, gateways and tag mapping.
Survey → go-live
Test strategy, automation and UAT support.
Build → release
Training, migration, floor support and help desk.
Go-live → ongoing
Technical due-diligence pack
Shared under NDA within [X] business days of request.
Request the pack →Logical and deployment diagrams
Pre-filled standard responses
What flows where, and where it is stored
APIs, connectors and event specs
Availability, performance, recovery
Controls, logging, data use
RPO, RTO and test approach
Scope, milestones and service levels
Roles, sub-processors, obligations
Data export and handover
No. Your ERP stays the system of record for finance and masters. We integrate with it and extend it to the shop floor — production, quality, maintenance and machine data — so nothing is ripped out.
Your data is always yours, in open, exportable formats. Ownership of custom code and configuration is set out in the SOW, and architecture, interfaces and runbooks are documented and handed over.
Yes. The platform is API-first with documented interfaces, decision records and admin guides. Knowledge transfer is part of every engagement, and your team can extend and integrate without us.
One data model with plant-level configuration: each plant keeps its own routings, recipes and approvals, while masters and group reporting stay common. New plants follow a repeatable onboarding playbook.
Yes. Run it in Trikaan cloud, a dedicated environment, your own cloud account, on-premise or hybrid with plant edge gateways — operated to the same standard. See Cloud & Hosting for the comparison.
Answers are grounded in your own records with links to the source rows, low-confidence outputs are flagged for review, and any action AI proposes waits for a named approver. Every prompt and approval is logged.
An assessment of one plant, a pilot on one plant or value stream with measured results, then a wave-based rollout across plants under a programme office with agreed SLAs.
OT integration is read-only through edge gateways that make outbound-only connections, segmented from your IT network. There is no write path from the cloud to your controllers.
