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Six projects · real numbers

The number that changed, and how

Client names are withheld where the work touches production, pricing or patient data — which is most of it. Everything else is exactly as it happened, including the projects where the most valuable finding was an error in the process we were replacing.

Cement · Data & Reporting

Four hours of manual EOD reporting to zero

Integrated cement group, Eastern India

The situation

Each plant produced its end-of-day report by hand — a person exporting from the historian, keying figures into a spreadsheet that had evolved over years, and emailing the result mid-morning. Three plants, three formats, and no way to reconstruct where a challenged figure came from.

What we did

Two days on site per plant for tag and report inventory, then AURA configured to read OPC tags read-only, apply their formulae, aggregate by shift and day, and publish their exact Excel template to the LAN drive. Parallel run against the manual process for five weeks.

4 hrs → 0
Daily manual effort per plant
06:30
Report on the drive, before the shift
3
Errors found in the legacy spreadsheet
100%
On-premise, LAN-only, read-only
AURA platformData engineeringCement industry
INSIDE YOUR FIREWALL — LAN ONLY, NO INTERNET PATHSOURCE SYSTEMSDCSPLCSCADAHistorianread-only OPCAURA ENGINEWindows service · unattendedValidate readingsApply your formulaeAggregate shift / day / MTDFlag deviation vs targetStore-and-forward bufferREPORT BUILDERyour exact Excel templateLAN SHARED DRIVEEOD_2026-08-08.xlsxEOD_2026-08-09.xlsxAudittag → formulaAD / ACLpermissions
Distribution · Custom Software

Three people re-typing orders, replaced by twenty minutes of exception handling

Industrial distributor, Howrah

The situation

Orders arrived by email, WhatsApp and phone, and three people re-typed them into Tally — frequently until nine at night. Pricing errors ran to several lakh a year because a nine-tier price list was applied by hand.

What we did

A costed process map ranked every process by hours consumed and money leaked. Order capture and dispatch came first: multi-channel intake, a pricing engine with the real slab and scheme logic, credit checks, and direct Tally integration with reconciliation reporting.

−78%
Manual data entry hours
7 months
Payback on labour cost alone
~0
Pricing errors after go-live
2 days
Month-end close, from eleven
Custom softwareERP developmentTally integration
SingleSource of TruthPostgreSQL · auditedFinanceGL · AP/AR · GSTProcurementPR → PO → GRNInventorybatch · bin · FIFOProductionBOM · routing · WOSalesquote → invoiceQualityIQC · IPQC · CoAMaintenancePM · breakdownHR Linkshift · payroll feed
D2C · Commerce

Same traffic, same ads, conversion from 1.1% to 1.9%

Home and living D2C brand, Kolkata

The situation

A bought Shopify theme carrying eleven apps, a 4.2-second mobile LCP, and attribution that disagreed with the bank account. The team was optimising spend against numbers that were quietly wrong.

What we did

App audit with a written verdict on each, then a headless Next.js storefront on Shopify with an image pipeline, a real search index, and a server-side event layer feeding a warehouse where cohort, LTV and contribution margin could be computed honestly.

+64%
Conversion rate
0.8s
Mobile LCP after rebuild
38%
Purchase events previously lost
2.3x
Repeat rate after identity resolution
eCommerceNext.jsAnalytics
storefront · Next.jsShopify / Medusacatalog · ordersPaymentsRazorpay · StripeSearchTypesense · AlgoliaLogisticsShiprocket · DelhiveryEvent Busevery click → KafkaWarehousecohorts · LTV · RFMRetention AIwin-back journeys
Steel · Plant Systems

A section running 5 points below yield for a year, found in six weeks

Rerolling mill, Durgapur belt

The situation

Yield was reported monthly as a single aggregate around 94%. Nobody could attribute loss to a section, a shift or a cause, so improvement conversations were general rather than specific.

What we did

Heat-level identity captured at existing material-handling points, yield decomposed into melting, casting, scale, crop and finishing, and each component attributed by shift, grade and section. Specific energy added at heat level with time-of-day tariffs applied.

+1.4%
Yield within two quarters
−6%
Specific energy consumption
−22%
Rejection rate
0
Capital investment required
Data engineeringManufacturingControl-room dashboards
Sinter / DRIburden mixBlast / EAFheat no. 4,182LRFchemistry trimCasterbillet / slabReheatsoaking curveRolling MillTMT / HR coilHEAT-LEVEL TRACEABILITY — every coil traced back to ladle chemistry, mould level and roll passYield +1.4%Sp. energy −6%Reject rate −22%Auto mill balance report
EPC · Data Collection

Fourteen months without missing a tender notice

EPC contractor, Kolkata

The situation

Two people checked government and PSU portals every morning and still missed notices — which in this business means missing a quarter. Dozens of portals, no common format, short response windows.

What we did

A monitored collection system across the portal set with strict field validation, per-field fill-rate alerting, document retrieval with text extraction, and a deduplicated structured digest delivered before 07:00 each day.

0
Notices missed in 14 months
07:00
Digest before the working day
~2 hrs
Median time to repair a portal change
2 people
Redeployed to bid preparation
Web scrapingData engineeringAutomation
FrontierURL queue · priorityHEADLESS BROWSER FARM — 240 concurrentProxy MeshIN · residentialSG · datacenterDE · mobileUS · residentialPolitenessrobots · rate · backoffAnti-bot logicfingerprint rotationParseschema mapValidatepydanticStoreS3 + Postgres12M pages / week99.1% parse yield
Distribution · AI

Supplier invoices: 94% straight through, 20 minutes of review a day

Distribution group, Kolkata

The situation

Two people spent most of the working day keying supplier invoices into the accounting system. Volumes were growing and the next step was a third hire.

What we did

A document intelligence workflow with a 150-case evaluation set built before any implementation. Extraction, validation against master data and GSTIN checks, confidence thresholds routing uncertain cases to a human, and every decision logged against the source document.

94%
Straight-through processing
6.5 hrs
Daily manual work removed
20 min
Exception review each morning
0
Additional hires needed
AI automationDocument intelligenceERP integration
Layout parsetables, stamps, handwritingField extractionGSTIN, HSN, qty, rateHuman reviewonly < 92% confidenceStructured output → ERPvendorOrient Cement Ltdinvoice_noOCL/26/00871date2026-08-04taxable₹ 18,42,300gst_18₹ 3,31,614confidence0.987
Why the numbers are specific

We record a baseline before we build

Improvement that was never measured against a starting point is an assertion. Every figure on this page has a before as well as an after.

They ran our new pipelines alongside the old ones for six weeks and found four places where our existing reports had been quietly wrong for years. That alone was worth the project.
ASAnirban SenHead of MIS · Multi-plant manufacturer
We knew our yield was around 94 per cent. What we did not know was that one section on night shift was running at 89, and had been for a year. Finding that paid for the entire system.
WMWorks ManagerRerolling mill · Durgapur belt
We had two people checking tender portals every morning and still missed things. Now we get a structured digest at 7 AM with attachments, and we have not missed a notice in fourteen months.
SBS. BhattacharyaBusiness Development Head · EPC contractor, Kolkata
Next step

Tell us the number you want to change.

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