Aerial photograph at dusk: a sorting plant with conveyors and baled output on the left, a road with collection trucks through the centre, a processing plant with silos beyond, and irrigated crop rows on the right. Live readouts are pinned to the sorting line, the collection fleet and a buying station.
- Sorting line40,000 events/day
- Collection fleet212 on route
- Buying stationlot 1,214 graded
We build and run production AI for the people who move, sort, process and grow things.
A working prototype on your data in six weeks, for a fixed €25,000. Then a production build on your systems, priced before it starts, and a named team that runs it.
Fully customised applications, built on the systems you already run, with the integrations, security and compliance that make them real. Scoped, priced and accepted on evidence, and the team that builds it is the team that runs it.
Most operational AI stalls between the pilot and the plant.
The pilot ran on an extract.
Production needs the live feed, with its late telematics, missing tickets, manual overrides and sites that go offline.
Nobody owned the last mile.
A score in a dashboard changes nothing. It has to land inside the route planner, the work-order system or the ERP the crews use.
The pilot team moved on.
When the people who built it are not the people who run it, the knowledge leaves with them and the system decays.
There was no point to say stop.
Open-ended pilots have no acceptance criteria, so they neither fail cleanly nor graduate.
The reasons are rarely the model. They are the four things above, and everything we build is designed to pass each one.
Your systems, one application, output that lands.
Read where the records live, reconciled with lineage, modelled for the decision, written back into the tools people already use, and run by the team that built it. No one changes how they work to feed a pilot.
Start with the systems you already run.
Telematics, weighbridges, sensors, cameras, phone lines, contracts and the ERP, read where they live and on their schedule, late and missing records included.
One application, built on your systems.
Reconciliation and lineage for every figure, models chosen for the decision, and the integrations, security and compliance that let it pass procurement.
Output that lands where work happens. A team that runs it.
Written back into the tools people already use, with a way to see when it is wrong. The team that built it monitors, retrains and extends it.
More than a prototype. Built to run inside your business.
Enhansd is not a dev shop that hands over a pilot. The application is deployed in your environment, with the models you choose, behind controls that decide what a language model may see. Twenty years of running systems for operators and five of production AI, in one team, working to an agreed outcome.
On your servers, in your cloud, or in ours under your control.
Deployed where your policy says it lives. Access is granted per person, per system, for the duration of the work, and revoked when it ends.
Your language models or ours, chosen per decision.
A model you already license, an open model inside your perimeter, or a provider under a data processing agreement with sub-processors listed. Swapped without a rebuild.
Confidential data is not passed to a model.
Where a language model is used it receives the minimum, with identifiers removed, through one gate that logs what went in and what came back. Most of the work is deterministic and never touches a model at all.
The application is the system of action, not a chat window. Connectors to the ERP, telematics, portals and phone systems; workflows with approvals and exceptions; a database with lineage for every figure.
ISO 27001 management system, change control, audit trail, and a security questionnaire answered in writing by the people who will run the system. Built to pass procurement, not to be explained around it.
Business and technology experience in one team. We agree the operational number in week one and work until it moves, with acceptance points you sign off, not a timesheet.
Two industries we know from the inside. One pattern that transfers.
We proved the pattern in waste and agriculture, two of the hardest places to make AI work, as custom builds rather than packaged products. The pattern is not industry-specific.
Routes, plants, weighbridges, claims and the compliance return.
- Routing and fleet systems for a 10,000-vehicle national operator
- Composition and contamination analytics on 40,000 camera events a day
- Hazardous-waste profiling, municipal booking integration, EPR and packaging reporting
Grower networks, buying stations, drying, milling and traceability.
- Field-to-shipment traceability for the EU deforestation rules
- Purchase planning and yield forecasting by region, grade and supplier
- Quality grading, laboratory workflow and buyer-standard compliance
Then any business. The six jobs we do, reading what nobody reads, capturing the expert, talking to thousands at once, watching the outside world, standing up evidence under questioning, and seeing and judging, exist in utilities, logistics, manufacturing, professional services and public bodies alike. The same path applies, built on your systems.
- 20years of engineering and managed services
- 700people, delivery from Hyderabad, commercial in London
- 15+countries served
- ISO 27001information security
- CMMIprocess maturity
What this has produced for operators.
Eleven deliveries for waste and environmental services operators. Four are production AI. The rest are the data, integration and infrastructure work that production AI stands on.
Annual revenue recovered from camera events.
40,000 daily camera events assessed for contamination and extra yardage, with 70 percent less manual review, for a national collection operator.
Fleet cost reduction, 20 percent better route density.
Routing and logistics systems for a 10,000-vehicle national operator, at 99.9 percent data accuracy.
Of email volume automated into 97-field profiles.
Profiles drafted from unstructured email and laboratory documents, validated and written into the portal, for a hazardous-waste services provider.
From design to production.
Public incident signals scored and routed to field operations, identifying a $58M addressable opportunity for a national environmental services operator.
Enhansd understood that waste logistics is not a generic routing problem. They helped us improve route density, modernise fleet systems and create better visibility between field operations and central planning.
All eleven deliveries
| Work | For | Built with | Result |
|---|---|---|---|
| Emergency-response lead intelligenceAI | A national environmental services operator | Serverless AWS, Bedrock, PostGIS entity matching, Twilio dispatch | Public incident signals scored and routed to field operations. $58M addressable opportunity identified. 14 days from design to production. |
| Contamination and extra-yardage detectionAI | A national collection operator | Vertex AI object detection and classification, BigQuery batch evaluation | 40,000 daily camera events assessed. $8.7M annual revenue recovery. 70 percent less manual review. |
| Hazardous-waste profile ingestionAI | A hazardous-waste services provider | Intelligent document processing, OCR, LLM validation, portal integration | 97-field profiles drafted from unstructured email and lab documents. 70 percent of email volume automated. |
| Tradeshow lead assistantAI | A commercial sales organisation | Mobile OCR, automated research, knowledge-base retrieval, CRM | Personalised first contact drafted in under 60 seconds from a business card. |
| Fleet routing and logistics | A 10,000-vehicle national operator | ASP.NET Core, batch geocoding, disposal optimisation, rolling audit tools | 15 percent cost reduction. 20 percent route-density improvement. 99.9 percent data accuracy. |
| Municipal bulky-waste integration | A municipal collection programme | Standardised booking APIs, permit verification, real-time SLA reporting | 90 percent faster go-live for contractors. 40 percent less build effort. Five materials facilities live. |
| Operations data modernisation | A global waste enterprise | Snowflake medallion architecture, Azure Data Factory, Informatica | 80 percent faster reports. 95 percent fewer ETL failures. Hundreds of tables migrated. |
| Waste profiling and self-service | A major collection provider | Node.js, Angular, unified profiling services | 30 percent fewer support calls. 160,000 paperless transitions. |
| Zero-downtime infrastructure migration | An international waste firm | Storage, virtualisation and network modernisation; Splunk, ITSI, DPA | 100 percent uptime through migration. 50 percent reduction in mean time to recover. |
| Identity, threat and audit readiness | A publicly traded waste corporation | SailPoint, Secret Server, SIEM, EDR, DLP | Audit compliance achieved. 60 percent faster response. |
| Financial document repository | A multi-platform finance function | API Gateway, Lambda, pre-signed S3, DynamoDB indexing | Sub-100 ms retrieval at 120,000 daily requests. No idle compute cost. |
Three steps, a fixed price for each, stated before work starts.
The price of each step is written down with what it includes, before work starts, and does not move unless you change the scope. Discovery is €25,000. You decide after each step, on evidence.
Discover.
One use case, a working prototype on a slice of your data, and a costed, sequenced plan for the build.
Build.
A production application on your stack, fully integrated, with milestones and acceptance points.
Run.
A dedicated, named pod that operates, monitors, retrains and extends it.
One price per step, in euros, written down with what it includes and what it does not. Nothing is added without your agreement, and the plan you leave discovery with prices every build milestone.
In week one we agree the operational number the work has to move and the acceptance criteria. You see progress weekly with the people who run the process, and we work with you until the outcome is met, not until the hours run out.
No long contract up front. After discovery you have the prototype and the plan whatever you decide next. Run is monthly.
Six things we have built or would build, shown working.
One per job, each with its twin in the other industry. Every demonstration is a working application on demonstration data for a fictional operator. The first screen is open to everyone; the working screens ask for an email address.
Live figures come from each demonstration's own data.Your operation is the seventh. Tell us the problem.
Forty-five minutes with an expert.
You bring one operational problem and talk to one of the people below. You leave with a one-page view of where AI would land in your operation, what a €25,000 discovery sprint would cover for you, and what the build after it would cost. No follow-up sequence.
Kim BatchelerFounder and CEOTwenty years building systems for operators, the last five in applied AI, from the first production models to the assistants on this site.
Suresh Kumar UdathaChief AI OfficerOwns AI strategy, governance and data privacy for every engagement, and leads the engineering that delivers it.
Manod JinnuriChief Operating OfficerRuns delivery: discovery sprints, acceptance points, the named pods and the commitments that go in writing.
Syed Kashif AliNew Business LeadThe first conversation for most operators. Scopes the problem, brings the right engineer to the call, keeps the first step small.
Murali LekkalaForward-Deployed EngineerEmbeds with your team and writes the production code that connects the application to the systems you already run.
Durga Satish VeeravasarapuAI Platform EngineerRuns the infrastructure underneath, the serving stack, compute and data pipelines, sized for production volumes.
Snehasis MisraAI-Native EngineerTakes a use case from business intent to shipped feature, specifying, generating, reviewing and validating the code.
Sandeep LingarkarApplied AI EngineerSelects, tunes and tests the models inside the application so they work reliably for your specific decision.
We reply within two working days, from London or Hyderabad, and the first conversation is with the people who would do the work.
Download the deck PDF, ten pages, 5 MB