When intelligence
needs to live
where the data is.
Edge AI and on-device inference: AI models running directly on embedded hardware, with no cloud roundtrip, no internet dependency, and sub-100 ms response times, built for operational environments where latency and connectivity cannot be assumed.
Ask AI about this service
Deploying AI to the cloud and piping data to it works for applications where latency is acceptable, connectivity is reliable, and data privacy is not a concern. None of these conditions hold in most operational environments. A camera detecting an intruder cannot wait 200 ms for a cloud roundtrip. A sensor on a vehicle in a tunnel cannot wait for a 4G signal. An asset in a defence installation cannot send raw data to a public cloud endpoint. Edge AI solves these problems by moving the model to the data, not the data to the model.
What we build.
Model Optimisation for Edge
Quantisation (INT8, FP16), pruning, and knowledge distillation turn cloud-trained models into edge-deployable ones that keep production-grade accuracy at a fraction of the compute cost.
Edge Hardware Selection
We choose the compute platform, from microcontroller-class inference to GPU-class edge, by workload, power budget, environment, and cost, then validate it against the actual model before committing.
On-Device Interface Design
LCD HMIs, LED status displays, audio alerts, and rugged touchscreens that give field operators the information they need without a connected remote dashboard.
Edge-to-Cloud Architecture
Hybrid architectures that run time-critical inference locally, buffer events during connectivity gaps, and sync only structured events and aggregates to central systems when a connection is available.
A model running on target hardware in 2 weeks.
From the day you engage us, not from a kickoff weeks later. Every milestone is a result you can see.
- Day 4
Compute platform and latency budget fixed
- Day 14
Quantised model benchmarked on the real device
- Day 45
Field-ready runtime with update and rollback
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Fixed scope
Scope, price, and milestones are agreed before work starts.
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No surprises
Weekly demos and plain-language status. Changes are priced before they are built.
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Compliance from day one
On-device by design: no data leaves the unit unless you choose, and model updates are signed and auditable.
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You own the IP
Code, models, and documentation transfer to you on successful handover.
Know the price, the date,
and the exit before we start.
Fixed-scope project. A defined deliverable at a fixed price and date. Payment is released as each milestone is accepted. The same terms are written into every statement of work for edge ai & on-device interface, so there is nothing to discover later.
- Quote Within 5 working days Price per milestone, dates, and named architect
- Priced One price per milestone Third-party costs listed separately, at cost. Invoiced in USD for international clients
- Paid On accepted working software Not on calendar dates or hours logged
- Changes Written and priced first Cost and date impact before work starts
You fund accepted work, never a promise.
Drag to move through the engagement. Money is released only as each gate is accepted, so what you have paid never runs ahead of what you can use.
- Milestone 1Day 4
Compute platform and latency budget fixed
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 20% - Milestone 2Day 14
Quantised model benchmarked on the real device
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - Milestone 3Day 45
Field-ready runtime with update and rollback
Accepted whenWorking software demonstrated against the written acceptance criteria.
Releases 30% - HandoverAt final acceptance
Code, architecture documents, deployment scripts, and credentials transferred to you.
Accepted whenFinal acceptance signed. Defects then fixed under warranty at no charge.
Releases 20%
Illustrative split. The exact percentages and dates are fixed in your statement of work.
When something goes wrong, who pays?
Every project meets surprises. What matters is who carries them. Pick one and compare how three commercial models respond.
The hours keep running while the team investigates. You pay for the surprise.
A change request is raised, usually priced at a premium and argued over.
We verified it before quoting, and the risk register held an allowance. Any overrun beyond it is ours.
It is absorbed into the hours. The date slips quietly and you learn at the invoice.
A change fee arrives, often as a lump sum with little explanation.
You get a written amendment with cost and date impact. Nothing starts until you approve it.
You pay for the difference. The estimate was only ever a guess.
The vendor reopens scope to recover margin, or cuts corners to hold it.
The price stays fixed. The overrun is ours to carry, and you see the budget position at every demo.
It is billed on the calendar anyway. The status report says "on track".
The invoice follows the date in the contract, whether or not the work is done.
Payment for that milestone is held until it passes the written criteria. You do not fund a miss.
One price, built in the open.
The proposal itemises every feature or milestone, names its assumptions, and keeps a risk register with allowances. Allowances you never use are not billed.
- Hardware readiness and number of boards
- Certification or standards needed
- Connectivity and fleet size
- Test and field-validation scope
Working hardware samples are with us by Day 5.
If falseDates move by the delay. The price does not change.
The hardware interfaces match the datasheets provided.
If falseA costed change request with date impact, before work continues.
Field-validation sites are available on the dates agreed.
If falseRe-scheduling is planned with you and written down.
- Dev boards and test hardware
- Cloud and connectivity usage
- Certification lab fees
Invoiced in USD for international clients, with the currency fixed in the SOW. You own it. Code, models, documents, and credentials transfer on payment.
Built on a production-grade stack.
We pick tools proven in production and maintainable over long lifecycles - not whatever is trending. These are a few we reach for here; explore the full stack behind our engineering.
Explore our technology stack →
What buyers ask before they commit.
Straight answers on timelines, price, risk, and ownership, the same terms that appear in every proposal.
01What is Edge AI & On-Device Interface?
Edge AI and on-device inference for industrial, railway, and defence applications. Run AI models on embedded hardware without cloud dependency, latency, privacy, and offline operation by design.
02How quickly does Edge AI & On-Device Interface deliver a first result?
A model running on target hardware in 2 weeks. Typical milestones are Day 4: compute platform and latency budget fixed; Day 14: quantised model benchmarked on the real device; Day 45: field-ready runtime with update and rollback. Exact dates are written into the statement of work before you commit.
03How is Edge AI & On-Device Interface priced?
Fixed-scope project: one price per milestone, with a first-release date, itemised in a statement of work within five working days of scoping. Payment is released as each milestone is accepted against written criteria. Cloud hosting, licences, and model usage are passed through at cost. International clients are invoiced in USD. The main price drivers are hardware readiness and number of boards, certification or standards needed, connectivity and fleet size, test and field-validation scope.
04What happens if the scope changes or the work overruns?
The price stays fixed. A change to scope is quoted in writing, with its cost and date impact, and starts only after you approve it. If our own estimate was wrong, the overrun is ours, not yours. You see the budget position against milestones at every demo.
05Who owns the code, models, and documentation?
You do. Code, models, and documentation transfer to you on successful handover. Code, architecture documents, deployment scripts, and credentials transfer on payment, with no licence back.
06How are security and compliance handled?
On-device by design: no data leaves the unit unless you choose, and model updates are signed and auditable.
07Do you work with clients outside India?
Yes. We are based in Noida and work with clients around the world, delivering remotely. Calls and demos are scheduled in your time zone, proposals and contracts are in English, international clients are invoiced in USD, and every engagement follows the same fixed-scope terms regardless of where you are.
08When would you decline to fix a price?
If the scope is not yet defined, we do not guess a number. We propose a short, time-boxed discovery with its own fixed price, then quote the build.
AI that works without the cloud.
If your operational environment has latency requirements, connectivity constraints, or data sovereignty requirements that make cloud AI impractical, we can design an edge AI architecture around your specific hardware, inference workload, and operational context.