Manufacturing
Visual quality checks on the line and forecasts of machine maintenance need from sensor history.
Applied AI
AI as a Service
Practical artificial intelligence delivered as dependable services your systems can call. We build, host and maintain models for prediction, classification, vision, speech and document understanding, wrapped in APIs your team can rely on.
2 to 5 weeksfrom data to a first model in production
Under 200mstypical inference latency for real time endpoints
Weeklyautomated checks for drift and accuracy in production
Per callpricing so cost tracks usage, not headcount
What AI as a Service means here
A model is only useful when it runs reliably every day
Building a model that scores well on a test set is the easy part. The value comes from serving it fast, watching it for drift, retraining it as the world changes and giving your team a clean way to call it from the software they already use.
We take a business question, work out whether AI is the right tool, and if it is, deliver an endpoint your systems call like any other API. Behind that endpoint sit the data pipelines, the model, the monitoring and the retraining schedule.
If a simple rule or a small statistical model solves the problem, we will build that instead. The aim is the outcome, not the buzzword.
Where clients apply it
A cross section of the problems AI as a Service has solved for Spykra clients.
Healthcare
Triage support, signal analysis and document extraction from referrals and results, with clinical oversight.
Financial services
Risk scoring, transaction monitoring and statement parsing with clear reason codes for every decision.
Retail and consumer
Demand forecasting, personalised recommendations and automatic tagging of product imagery.
Logistics
Estimated arrival prediction, damage detection from photos and route demand modelling.
Operations teams
Ticket classification and routing, contract review assistance and forecasting of workload and staffing.
Prediction and forecasting
Turn history into a usable estimate of what comes next
Demand, churn, lifetime value, maintenance need, credit risk, staffing levels. We build models that produce a number and a confidence range your planning can act on.
Each prediction comes with the main factors behind it, so a person can sense check the output and explain it to a customer or a regulator.
- Time series forecasting with seasonality and events
- Churn and propensity scoring with reason codes
- Feature stores so training and serving use the same data
- Backtesting against real outcomes before launch
01Model lifecycle
- Data
- Versioned features, validated on ingest
- Training
- Reproducible runs, tracked experiments
- Serving
- Batch or real time, autoscaled
- Watch
- Drift, accuracy, latency alerts
Document intelligence
Read, classify and extract from the paperwork that slows you down
Invoices, contracts, forms, ID documents, statements and letters. We build pipelines that recognise the document type, pull out the fields that matter and route the result into your workflow.
Low confidence cases go to a human review screen instead of failing quietly, and every extraction keeps a link back to the exact place it came from in the source.
- Layout aware extraction for tables and key value pairs
- Human in the loop review for low confidence items
- Redaction of personal data where required
- Straight through processing rates reported per document type
02Extraction pipeline
- Ingest
- Email, upload, scanner, API
- Understand
- Classify, detect layout, read text
- Extract
- Fields, tables, signatures, stamps
- Deliver
- Validated JSON into your system
Vision and speech
Make sense of images, video and audio at scale
Quality inspection, object counting, damage assessment, safety monitoring, transcription and voice analytics. We train and host models that process media in real time or in bulk.
Models run in your cloud or at the edge depending on privacy, latency and connectivity, with the same monitoring and retraining discipline as everything else we build.
- Detection, segmentation and classification for images and video
- Transcription and speaker separation with domain vocabulary
- Edge deployment for low latency or offline use
- Active learning to improve accuracy from real cases
03Deployment options
- Cloud real time
- GPU endpoint, autoscaled
- Cloud batch
- Queue and worker pool
- Edge
- Quantised model on device
- Hybrid
- Edge inference, cloud retraining
What comes with every AI service
The engineering around the model that keeps it trustworthy in production.
Reproducible pipelines
Data, code and model versions are tracked together, so any result can be rebuilt and any regression can be traced.
Drift and quality monitoring
Inputs and outputs are watched for distribution shift and accuracy decay, with alerts before users notice.
Retraining on a schedule
New labelled data flows back in and models are retrained, evaluated and promoted only if they beat the current one.
Privacy and governance
Data minimisation, access control, retention rules and a record of what the model was trained on.
Latency and cost budgets
Endpoints are sized to a target response time and a cost per thousand calls that you sign off.
Explainability
Feature attributions and confidence scores on every prediction, plus model cards that describe limits and intended use.
How an AI service is delivered
A path that proves value on real data before you commit to production.
Frame the problem
We define the decision the model will support, the data available and the accuracy and speed needed to be useful.
Prove it on your data
A short build produces a model evaluated against real outcomes, with an honest read on what it can and cannot do.
Productionise
Pipelines, an API, monitoring and retraining are put in place, with load and failure testing before go live.
Operate and improve
We run the service, watch the metrics and feed new data back in, reporting accuracy and cost against the targets we agreed.
Frameworks and infrastructure
Modelling
PyTorchscikit-learnXGBoostHugging FaceONNXOpenCV
Serving and MLOps
FastAPITritonRayMLflowFeastBentoML
Infrastructure
AWS SageMakerGCP Vertex AIKubernetesNVIDIA GPUsS3Kafka
Questions about applied AI
How do we know if AI is the right tool for our problem?
We assess that first, for free. Many problems are solved better by a rule, a lookup or a small statistical model, and we will tell you when that is the case. AI earns its place when patterns are complex, data is plentiful and the decision repeats often.
What data do we need to get started?
Enough labelled examples of the outcome you want to predict or extract, and access to the systems where that data lives. For many tasks a few thousand good examples are enough for a first useful model. We help you assess and prepare what you have.
Where does the model run and who can see the data?
By default everything runs in your cloud accounts and your data stays with you. For sensitive work we can deploy on premise or at the edge so data never leaves your network.
What happens when the model gets worse over time?
We expect it and plan for it. Monitoring watches for drift and accuracy decay, and a retraining pipeline refreshes the model on new data. A new model is only promoted if it beats the one in production on held out tests.
How is this priced?
A fixed price to build and prove the first model, then a monthly platform fee plus a usage charge per thousand calls. Cost tracks how much value you are getting rather than how many people are involved.
Bring a decision you make often
If it repeats, depends on patterns and has data behind it, there may be a service in it. Tell us the problem and we will tell you honestly whether AI helps.
Start a project Spykra Technologies UK Ltd, London and Mumbai.