Dhyuthi Infratech is a deeptech AI company building computer vision, optimization, and large-language-model platforms for pharmaceutical manufacturing, construction and infrastructure, logistics, and multi-site industrial operations.
Bulk drug plants, large infrastructure projects, distribution networks, and multi-site industrial operations still run on stranded data, paper-bound workflows, and tribal knowledge.
Dhyuthi Infratech was founded to close that gap. We build AI-native software that ingests the unstructured reality of these operations — CCTV streams, engineering drawings, freight invoices, batch records, sensor feeds, and inspection reports — and turns it into a continuous operating picture.
Our customers are operators who cannot afford to be wrong. We build accordingly: every model is auditable, every recommendation is traceable, and every workflow is designed for the field — the reactor hall, the construction site, the loading bay — not the showroom.
Dhyuthi solutions are sold as standalone products or composed into a single operations stack for enterprise deployments.
Real-time vision models running over existing CCTV and inspection imagery — for fire and hazard detection, GMP compliance, defect detection, inventory counting, and as-built verification against engineering drawings.
LLM-powered extraction across drawings, manuals, code books, freight invoices, batch records, and SOPs — with citation-grade provenance. Powers our engineering-assistant chatbot and spec, BOM, and quote engine.
A real-time operating picture for distributed operations — plants, project sites, depots, fleets. Multi-source data fusion, exception workflows, predictive forecasting, and an embedded LLM assistant for natural-language operator queries.
A long-haul pipeline is inspected by a smart pig — an in-line inspection (ILI) tool flowing with the product, recording MFL, UT, EMAT, and IMU signals across the entire length. A single run produces tens to hundreds of millions of anomaly records. Operators end up with stacks of ILI tallies from different vendors, in different formats, with different classifications — and no calibrated way to ask the single most important question: is this defect growing fast enough to fail before the next inspection?
Dhyuthi's NDT Defect Growth Platform is the analytical layer that closes the gap. A software-defined, Bayesian, physics-informed inference engine that ingests run-on-run ILI data — across vendors, across years, across tool generations — and produces calibrated, audit-grade per-defect intelligence aligned to PHMSA, API 1163, ASME B31.G, and Modified MAT-8 / API 579.
30+ ILI service providers each describe "dent with metal loss" — and its cracks, gouges, weld features — in their own language. A supervised classifier trained on decades of multi-vendor ILI reports maps free-text and code-based anomaly types into a unified canonical taxonomy. A 2014 GE run, a 2018 Rosen run, and a 2022 Baker Hughes run finally speak to each other.
Anomaly #47,238 from 2014 and anomaly #51,002 from 2022 are the same physical feature — or aren't. Probabilistic matching combines odometer drift correction, feature-shape similarity, depth profile, and orientation-reference reconciliation across tool generations. Without this, you cannot compute a growth rate; with it, every defect carries its full historical trajectory.
ILI tools have stated depth-sizing tolerances of ±10% wall thickness at 80% confidence under API 1163. A measured "growth" of 5% may be entirely measurement noise. A hybrid Paris-Erdogan / fracture-mechanics core with hierarchical cohort priors and a sparse-variational GP residual separates real growth from instrument noise — producing a posterior over true growth rate, not a single point estimate.
PHMSA (49 CFR 192 / 195), Canadian CER, and similar regimes require a formal Integrity Management Program, justified dig choices, and reproducible audit trails. Every recommendation persists with its source observations, model version, cohort assignment, posterior parameters, and calibration metrics — reconstructable on any past date, aligned to IEC 62278 RAMS expectations.
Coordinate ILI runs, cathodic-protection surveys, hydrostatic testing, and direct-assessment digs. Prioritise by composite risk and ensure coverage of regulated segments — High Consequence Areas, Moderate Consequence Areas, and operator-defined sensitive zones.
Run normalised ILI data through engineering criteria — ASME B31.G, Modified B31.G, RSTRENG for corrosion; MAT-8, Modified MAT-8, Raju-Newman, Log Secant, and API 579 for cracks — to identify immediate, 60-day, 180-day, and one-year repair conditions under PHMSA.
Generate dig sheets for every anomaly that meets repair threshold. Track scheduling, NDE field verification, repair, and closeout. Field findings flow back into cohort calibration — every dig sharpens future predictions.
Watch SCADA pressure history, CP voltages, and known-anomaly behaviour between ILI runs. Built-in API 1163 ILI Performance Validation compares tool calls against field NDE — telling you whether your vendor's tool actually met its stated tolerance, run by run.
PHMSA 49 CFR 192 / 195 · API 1163 · ASME B31.8S · B31.G · Modified B31.G · RSTRENG · MAT-8 / Modified MAT-8 · API 579 · Raju-Newman · Log Secant · IEC 62278 RAMS
Internal & external corrosion · stress-corrosion cracking · fatigue cracking · mechanical damage · weld defects · geohazards (IMU bending strain) · combined & interacting threats
SaaS multi-tenant with per-operator tenant isolation · sovereign-cloud or operator-private cloud · ILI-vendor-grade data security · audit-grade lineage on every recommendation
Each of the use cases below is sold individually or composed into a sector-level platform. Status reflects the current commercial stage — production deployments, active pilots and POCs, in-build, and concepts open to design partners.
Computer-vision early-fire and smoke detection over existing CCTV in reactor halls, solvent yards, and tank farms. Sub-second latency with false-alarm suppression for steam, flares, and venting; integrated with SCADA and fire panels.
Real-time gowning, behaviour, line-clearance, and equipment-cleaning verification across clean rooms, with timestamped video evidence per deviation. Reduces deviation reports and QA supervisory burden under cGMP.
Hybrid first-principles + ML model of reactors, distillation, centrifuges, and utilities. Engineers simulate parameter changes, predict yield and impurities, and run golden-batch comparisons before committing to the floor.
Telematics + ML platform for tippers, dumpers, tankers, and pavers. Route optimisation, idling detection, fuel-flow anomaly detection, and ERP-linked material reconciliation. 8–15% fuel reduction in pilots.
Vision-based counting of cement bags, steel-rod bundles, and aggregate stockpiles at gates and godowns. Auto-GRN and BOQ reconciliation. ~80% reduction in manual stock-counting effort plus pilferage deterrence.
Vision-based classification of weld defects (porosity, undercut, slag, cracks) and rebar tying / lap / cover audits. Geo-tagged defect logs and inspector mobile app. 5× faster inspection coverage vs manual sampling.
CV + 3D reconstruction that aligns site imagery to approved GFC drawings — verifying rebar count, spacing, lap length, formwork, embeds, and verticality before pour. Annotated discrepancy reports with severity grading.
AI extraction of BOMs from 2D PDFs, AutoCAD DWG, and selected 3D formats — rebar schedules, concrete volumes, structural sections, embeds, and finishes. 60–80% reduction in estimation effort.
Vision + LiDAR retrofittable kit for excavators, pavers, RMC trucks, and cranes. 360° awareness, person vs object classification, geofenced exclusion zones, graded operator HMI, and incident logging for BOCW / ISO 45001.
OCR + LLM extraction across heterogeneous invoice formats; rate-card matching, detention validation against e-POD events, duplicate detection, and discrepancy workflows. 2–5% recovery of freight spend at near-100% audit coverage.
Bill of Lading, freight invoice, and arrival-notice cross-reconciliation. THC, BAF, CAF, and FX validation; demurrage and detention computation against gate-in / gate-out; carrier and forwarder benchmarking.
Optimization engine for outbound warehouse logistics — 3D bin-packing with stack and fragility constraints, dock allocation, multi-stop routing with time windows, and dynamic re-optimisation for late orders.
Aggregates ERP, MES, SCADA, project tools, telematics, and IoT into one command interface for pharma plants and construction projects. Real-time KPIs, exception alerting, and an embedded LLM assistant for natural-language queries.
Multi-source fusion across IoT, GPS / telematics, EO / IR cameras, satellite, ERP / MES, and third-party data into one deconflicted operating picture. CV anomaly detection, geofencing, mobile-first incident workflow, and audit-grade event recording.
Retrieval-augmented chatbot trained on the customer's SOPs, drawings, code books (IS, ASME, USP, ICH, AWS), and project archives. Mandatory source citations, role-based access, and on-prem / customer-cloud deployment.
Natural-language requirement capture for industrial-equipment sales — generates validated BOMs, technical specs, costing, and quotes with margin guardrails. Quote turnaround cut from days to minutes; CRM-integrated.
Bayesian, physics-informed analytics platform for oil & gas pipeline operators. Ingests multi-vendor ILI runs (MFL, UT, EMAT, IMU), normalises across 30+ vendor formats, separates real growth from API 1163 tool tolerance noise, and produces audit-grade remaining-life posteriors aligned to B31.G / RSTRENG / MAT-8 / API 579. See the flagship section above.
Dhyuthi engages through targeted pilots, paid POCs, and strategic partnerships. We are selective about whom we work with, because our customers expect us to be.