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NXTVIS

Capabilities

Everything we build, and why.

Eight AI capabilities, assembled to fit what the audit finds. The audit decides which of these your operation actually needs, and in what order, before anyone writes code.

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Proof of concept in 2 weeksProduction in 2 monthsDeployed across 7 sectors

Core capabilities

Built to solve a named problem.

Each of these exists because a client had a specific bottleneck, not because the category looked good on a services page. Every entry lists what actually gets delivered, how long it takes, and the sectors it lands in most often.

01

Computer Vision

Almost every site we walk into already has the cameras. What it does not have is anyone watching them, which makes the footage a recording budget rather than a capability. We run inference on the existing RTSP streams, on edge hardware installed on site, so the capital cost is already spent and the video never leaves the building. The work that decides whether this succeeds is threshold tuning, not model selection. An alerting system that cries wolf twice a shift gets muted within a week.

Defect detectionTrackingANPR

What gets delivered

  • Per-camera baselines and detection zones tuned against your own footage
  • Edge inference box installed on site, no video leaving the premises
  • Alert routing into the VMS, alarm panel or channel your team already watches
  • A searchable incident log with the clip attached to each detection
  • A false-positive tuning period written into the engagement, not bolted on after

Proof of concept on your own feeds in 2 weeks, in production inside 2 months.

Lands most often in

RMG & ApparelHealthcareSecurityRetail & E-commerceLogisticsManufacturing
02

Language & LLM Systems

The difference between an assistant customers trust and one they abandon is grounding. An agent connected to your real order, policy and account data resolves questions; one running on a model prior produces polite deflection and a good-looking deflection metric. Bangla is handled as a first-class language here rather than through a translation hop. Every deployment ships with an escalation path that carries the full conversation to a person, because the exception route is half the product.

RAGBangla NLPSummarisation

What gets delivered

  • A retrieval layer over your own documents, tickets and records, not a generic index
  • Native Bangla and English comprehension and response
  • Escalation to a named human with the conversation and retrieved record attached
  • Conversation analytics showing what customers actually ask, ranked by volume
  • Web widget plus messaging-channel deployment

Grounded pilot on one queue in 2 weeks, live on the full queue inside 2 months.

Built with this

Lands most often in

HealthcareSecurityRetail & E-commerceManufacturing
03

Forecasting & Optimisation

This is the category with the cleanest before-and-after. Kilometres driven, fill rate, stockout days and realised margin are unambiguous numbers, so the value is not a matter of interpretation. Most of the data needed is already in your order, dispatch or production tables, which is why these projects tend to reach production first. The obstacle is usually adoption rather than accuracy: a route a driver does not trust is a route they will not follow.

DemandRoutingPricing

What gets delivered

  • A measured baseline for the target number before anything is built
  • A model trained on your own history, with seasonality and promotions folded in
  • Constraints your planners recognise: time windows, capacity, driver hours, tariffs
  • A re-solve path for when the day changes, not a single morning run
  • Recommendations delivered into the tool the planner already opens

Backtested model in 2 weeks, running against live decisions inside 2 months.

Built with this

Lands most often in

RMG & ApparelHealthcareRetail & E-commerceLogisticsFinance & BankingManufacturing
04

Document AI

The highest-volume manual work in most operations is reading a document and typing what it says into a system, frequently twice into two systems. Extraction is the easy half. The half that matters is validation: every field cross-checked against the order, shipment or application it belongs to, with anything below a confidence threshold becoming a visible exception rather than a silent error.

OCRExtractionValidation

What gets delivered

  • Extraction across your real document mix, including the badly scanned ones
  • Field-level validation against the record the document belongs to
  • A confidence threshold you set, with everything below it routed to a person
  • Write-back into the ERP, TMS or core system so nothing is re-keyed
  • An exception rate reported weekly, with the corrections fed back into the model

Accuracy measured on your own documents in 2 weeks, in production inside 2 months.

Lands most often in

RMG & ApparelHealthcareLogisticsFinance & Banking
05

Speech & Audio

Speech earns its place where hands are busy or a keyboard is the wrong interface: a clinician mid-consultation, an operator in gloves on a production floor, a support team whose calls are the only record of what customers actually want. Transcription is table stakes. The value is in what is done with the transcript: a structured note a clinician signs, a ranked list of the reasons customers called, a fault code retrieved without stopping the line.

ASRDiarisationVoice UI

What gets delivered

  • Transcription tested against your own recordings, accents and floor noise
  • Speaker separation where more than one person is in the room
  • A structured downstream artefact: a draft note, a call summary, a ticket
  • Human review and sign-off before anything is filed
  • Retention and consent handling agreed before the first recording is processed

Accuracy benchmark on your own audio in 2 weeks, deployed inside 2 months.

Lands most often in

HealthcareManufacturing
06

Anomaly Detection

Rules fire on last year's patterns and drown the people reading the alerts. A model that learns the shape of normal (per account, per camera, per machine) catches more of what matters and raises fewer alarms doing it. The design constraint is the analyst, not the detector: an alert without the evidence bundle attached costs as much time as it saves.

FraudIntrusionDrift

What gets delivered

  • A learned baseline per account, camera or asset rather than one global rule
  • Alerts ranked by risk, with the supporting evidence attached
  • A false-positive budget agreed up front and measured against
  • Drift monitoring, so the detector keeps working as the pattern moves
  • A feedback loop from analyst verdicts back into the model

Detection measured against your labelled history in 2 weeks, live inside 2 months.

Lands most often in

SecurityRetail & E-commerceLogisticsFinance & BankingManufacturing
07

Agentic Automation

An agent is worth building where a workflow spans several systems and a person is currently the integration layer. It reads the case, retrieves what it needs, takes the action and stops at the gate you set. Flagging a garment for re-inspection is a survivable mistake and needs no gate; approving a payment is not, and gets one. We design the exception path before the happy path, because an exception queue nobody watches is where trust in the system goes to die.

Tool useWorkflowsApprovals

What gets delivered

  • The workflow mapped as it actually runs, including the workarounds
  • Tool access scoped to the minimum the agent needs
  • Explicit approval gates on anything irreversible or financial
  • A full action log, so every decision can be reconstructed afterwards
  • A named owner and a monitoring dashboard at handover

One workflow end to end in 2 weeks, the queue running inside 2 months.

Built with this

Lands most often in

HealthcareRetail & E-commerceFinance & BankingManufacturing
08

MLOps & Edge Deployment

Models decay. The product mix changes, a supplier changes packaging, a camera gets nudged, a new fraud pattern appears. We have seen accurate systems become useless in eight months purely through neglect, after which the organisation concludes that AI did not work here. Every deployment ships with drift detection, a retraining path and a named owner, and runs wherever the data is allowed to be: edge box on the floor, your private cloud, or managed cloud where latency and sensitivity permit.

Edge GPUMonitoringRetraining

What gets delivered

  • Drift and accuracy monitoring live from day one, not added after an incident
  • A retraining pipeline with a documented trigger and rollback
  • Deployment where the data is permitted to be: edge, on-prem or private cloud
  • Documentation, dashboards and training so your team can run it
  • A cost comparison of edge versus cloud inference for your actual volume

Monitoring in place with the first deployment; retraining path inside 2 months.

Lands most often in

RMG & ApparelHealthcareSecurityRetail & E-commerceLogisticsFinance & BankingManufacturing

Already deployed: stitch defect detection · Bangla support agents · predictive maintenance · ANPR gate control · demand forecasting · clinical note drafting · route optimisation · PPE compliance · KYC document AI · crowd anomaly alerts · markdown optimisation · OEE bottleneck analysis · face-recognition attendance · claims coding assistance.

Not sure which

Let the audit pick for you.

Rather than choosing from a menu, let our engineers look at your systems and tell you which of these pays back fastest. It costs nothing.

Book your free AI auditExplore a sector playbook

Proof of concept in two weeks. Production in two months.