Skilled technicians were spending a third of their shifts on data entry—logging readings on paper and wrestling with tablets in loud, hands-busy environments. We built a voice AI platform that handled documentation hands-free in the moment. Productivity went up 40% without adding a single new hire.
Challenge
Industrial documentation isn't a tech-resistance problem; it's a context problem. Tablets and keyboards were designed for desks, not for workers with grease on their hands and eyes on a machine. Voice was the only logical solution for a hands-busy environment, but off-the-shelf tools had already failed them.
Solution
We built an industrial-grade platform that turns a technician's description of work into structured enterprise records in real-time.
Result
40%
Productivity increase
Timeline
18-week delivery
5 delivery phases
Team
5 specialist roles
Cross-functional delivery
Evidence
Anonymized
Project and post-launch operating period
Our client was a large industrial group with two divisions: a heavy manufacturing wing with a broad facility network and a large field services team. Their highest-cost employees were spending hours every day on repetitive admin: reading gauges, completing checklists, and raising maintenance tickets. In factories, this meant stopping work and removing gloves to use a tablet. In the field, it meant engineers doing paperwork in vans at the end of a long day instead of moving to the next job.
Client Operating Profile
Scope, visibility, delivery context, and trust signals
“Our best maintenance engineer knows things about equipment that took fifteen years to learn. Yet he spends three hours a day filling in forms. That's not a documentation problem. That's a waste of our most valuable asset.”
VP of Operations
Client
Confidential Manufacturing and Field Services client
Reach
Pan-India manufacturing and field services footprint
Surfaces
4 platforms
Evidence
anonymized
Client operating details, platform surface area, and validation signals that shaped the work.
Confidential Manufacturing and Field Services client
Anonymized public case study
Established Industrial Group
Enterprise-scale workforce across facilities and field sites
Pan-India manufacturing and field services footprint
iOS & Android App, Smart Glasses Integration, Web Dashboard, SAP/ERP Integration
1998
anonymized
Project and post-launch operating period
Metrics are shown as client-reported or operating-period outcomes; confidential identifiers are removed where required.
Industrial documentation isn't a tech-resistance problem; it's a context problem. Tablets and keyboards were designed for desks, not for workers with grease on their hands and eyes on a machine. Voice was the only logical solution for a hands-busy environment, but off-the-shelf tools had already failed them.
Field engineers often completed reports 90 minutes after the work was done—usually from memory while sitting in a van. This delay led to high error rates in technical readings, which compromised warranty claims and future diagnostics.
Factory inspectors had to stop, handle a clipboard, and restart their task 60-80 times per shift. Each cycle wasted 40 seconds. Over a full shift, this added up to nearly an hour of non-inspection time per person.
Because reports weren't filed until the end of the day, the central SAP system was always hours behind. Schedulers were assigning parts and labor based on a reality that no longer existed.
New hires spent 3 weeks learning form sequences and ERP navigation rather than technical engineering. Senior staff were wasting time training juniors on data entry instead of technical expertise.
A prior pilot used a generic speech-to-text tool that couldn't handle industrial noise and didn't understand technical jargon. It required so many manual corrections that workers abandoned it within six weeks.
They bought ruggedized tablets, which were durable but didn't change the stop-start workflow. They even hired extra admin staff to 'scribe' for engineers, which reduced the burden but added massive headcount costs and introduced a new layer of communication errors.
"The VP of Operations saw the gap between a record and the truth. He knew that a report written three hours late was a compromise on quality. He needed a system that captured the truth in the moment it happened, without frustrating his best workers."
We didn't build this in a lab. We spent a week on factory floors and in service vans to understand what 85dB noise actually feels like and how engineers talk when their hands are busy.
We instrumented real environments to record ambient noise frequency signatures. We interviewed workers, from apprentices to veteran operators, asking one question: 'What do you wish you could just say out loud and have the system understand?' We found that the documentation problem wasn't a lack of will; it was a mismatch of tools.
Previous tools treated voice as a keyboard replacement—forcing workers to say things like 'Field: Temperature, Value: 65'. We realized documentation should be a byproduct of work. The system needed to listen to natural conversation and extract the data itself, leaving the worker's mind on the machine.
Natural language, not commands. If an engineer has to memorize 'command phrases,' the system will be abandoned. Furthermore, the system must work offline. An enterprise tool that dies in a basement plant room or a remote field site is just an expensive prototype.
We built an industrial-grade platform that turns a technician's description of work into structured enterprise records in real-time.
Achieves 95% accuracy in 85dB environments. It was trained on the specific frequency signatures of the client's air tools, ventilation systems, and assembly lines.
Generic tools fail in factories because they expect quiet rooms. Our model 'filters' out the machinery noise, ensuring technicians don't have to shout or repeat themselves.
Fine-tuned Whisper model with a custom spectral subtraction pipeline. Inference runs on-device to eliminate network latency.Base STT model trained on industrial-specific noise datasets
Domain-specific intent extraction for maintenance and inspection tasks
Cross-platform mobile app with robust offline-first sync capabilities
Native integration with core enterprise systems without custom SAP dev
Encrypted on-device storage with central audit and conflict logging
Auto-scaling cloud infrastructure for connected modes and voice archives
“Early versions read back robotic field names. Workers ignored them. We changed the UI to say: 'Got it—left bearing at 65, greased.' Workers felt understood, and correction rates plummeted.”
“If the AI is unsure about three values, it only asks about the most important one first. We found that in busy environments, workers will answer one quick question but ignore a list of three.”
Industrial AI fails when it's built in a quiet room. We structured the build so that the acoustic and NLU models were forged in the actual noise of the client's production lines.
Operational Log
Shadowed crews to record 80 hours of speech in 8 distinct noise profiles. Audited SAP APIs and collected voiceprints from 40 volunteer workers.
Iterative fine-tuning of the Whisper model against facility recordings. Annotated 50,000 speech transcripts to map technical shorthand to system entities.
Built SAP and Salesforce adapters. Optimized the models to fit on mid-range Android devices. Security-reviewed the voice biometric layer.
A pilot group used the system across multiple sites. They used voice and paper in parallel to prove data parity. The AI was retrained daily on live edge cases.
Phased launch across the facility network. Onboarded workers with hands-on training. Activated the live SAP write-back layer.
Deployed Roster
Working Rhythm
We turned six senior maintenance engineers into 'Domain Annotators.' They helped us define that 'running hot' meant a specific temperature anomaly. By paying them for their expertise and embedding them in the dev process, we ensured the system spoke their language, not our engineers' language.
Diagnostic Log
Start-stop noise. The model worked in steady-state noise but failed when a machine suddenly powered up or compressed air hissed nearby.
We returned to the floor to record 12 additional hours of 'transient noise' events. We retrained the suppression layer on this augmented data, and accuracy in the processing plant jumped from 81% to 93%.
Silent SAP rejections. A custom validation layer in the client's SAP instance was rejecting records without sending an error code back to our API.
We built a 'Validation Pre-check' into our adapter. It replicates the client's custom SAP logic locally, catching errors before they are sent and telling the worker exactly what needs to be changed in plain language.
Technical Shorthand. Experienced 20-year vets used highly idiosyncratic slang for certain parts that the generic model couldn't map.
We built a 'Personal Glossary' feature. It allows the system to learn per-worker shorthand, mapping a technician's specific verbal 'quirks' to the standard company part-numbers.
The hard numbers were undeniable, but the cultural win was a first for the group. For the first time in memory, worker satisfaction with 'internal tools' moved from the lowest-rated item on the survey to a top-three favorite.
tasks completed per shift across trained workers
fewer corrections needed in voice-created vs manual records
weekly administrative hours reclaimed for technical work
Manufacturing Group Client
Valuable lessons and strategic insights uncovered through this project that inform our future work and architectural decisions.
Industrial AI cannot be validated in a lab. The difference between failure and 95% accuracy was the week we spent recording machine start-stop cycles. Acoustic data collection from the client's specific floor is a mandatory engineering step, not an optional one.
If the confirmation is robotic, workers stop checking it. By using natural language confirmation, we kept workers engaged in the loop. The confirmation is the only safety valve to prevent an error from reaching the ERP—it must be human-centric.
In enterprise voice, it's not about being perfect; it's about what happens when the AI is wrong. By setting expectations and giving workers the power to easily correct the system, we turned skeptical veterans into advocates.
Running an operation where your best people are spending shifts on paperwork? That's recoverable time—usually more than anyone has formally calculated.
We build industrial voice AI for environments where generic tools fail. We know what it takes to get noise resilience and ERP integration right. Tell us about your field environment, and we'll give you an honest view of what's possible.
"No quiet-room demos. A real conversation about your floor and your workers."