AI, ML & GenAI
AI Solutions
Applied AI that earns its keep: document intelligence, predictive models, forecasting and conversational assistants with agreed accuracy targets and human-in-the-loop review.
Overview
What this service delivers
We start from a business decision that needs improving, not from a model. Once the decision, data and success metric are clear, we build the pipeline, train and validate the model, and put it behind an interface the people who make that decision actually use.
Every model ships with monitoring, drift detection and a retraining plan, so accuracy holds up months after go-live rather than degrading quietly.
Our Offerings
Where we plug in
Pick a single capability, or combine them into one accountable engagement. Scope, team shape and commercials adapt to your roadmap.
Machine learning models
Classification, forecasting, scoring and anomaly detection trained on your operational data.
Document & OCR intelligence
Invoice, KYC and form extraction with validation rules and exception queues for human review.
GenAI copilots & chatbots
Retrieval-grounded assistants over your own content, with citations and guardrails.
Predictive analytics
Demand, churn, credit-risk and maintenance forecasting surfaced directly in business dashboards.
Intelligent automation
Straight-through processing that combines rules, ML and workflow to remove repetitive effort.
MLOps & data engineering
Feature pipelines, model registries, CI/CD for models and continuous accuracy monitoring.
Our Expertise
Technology depth behind the service
Certified engineers, architects and QA specialists working across the stack, backed by reusable accelerators from 16+ years of enterprise delivery.
ML
GenAI
Data
Ops
Research
Research
Experimentation is time-boxed and evidence-driven. We build a baseline model quickly, compare approaches on a held-out set, and stop as soon as one clears the agreed threshold, including the option of a rules engine when it beats machine learning.
- Baseline plus candidate models compared on a frozen evaluation set
- Error analysis by segment to expose bias and edge-case weakness
- Documented model card: metrics, limitations and intended use
Concept & Ideation
Concept & Ideation
Every AI engagement starts by naming the decision to improve and the cost of getting it wrong today. Only then do we check whether the data exists to improve it. If it does not, we say so early, a data-readiness sprint is cheaper than a model that cannot be trusted.
- Use-case framing: the decision, the baseline, and the value of accuracy
- Data-readiness assessment on volume, labels, history and bias risk
- Feasibility verdict with success thresholds agreed before build starts
Design & Development
Design & Development
A model only creates value inside a workflow. We build the ingestion pipeline, the inference service and the interface the user sees, with confidence scores, human review queues and a clear override path for low-certainty cases.
- Feature and ingestion pipelines with versioned, reproducible datasets
- Inference APIs plus human-in-the-loop review and override screens
- For GenAI: retrieval grounding, citations, prompt evaluation and guardrails
Test Engineering
Test Engineering
AI testing goes beyond pass/fail. We hold a golden dataset, run regression on every model change, red-team prompts on generative features, and verify that fallbacks behave sensibly when the model is unavailable or uncertain.
- Golden-dataset regression on every model or prompt change
- Fairness and segment-level accuracy checks with documented outcomes
- Adversarial and prompt-injection testing on generative interfaces
Re-Engineering
Re-Engineering
We also rescue stalled AI work: notebooks that never reached production, or models whose accuracy has quietly drifted. The fix is usually industrialization: proper pipelines, evaluation and monitoring around logic that was already sound.
- Notebook-to-production refactoring with tested, scheduled pipelines
- Re-training and re-validation of drifted models against fresh data
- Cost optimization on inference, storage and token consumption
Support & Reliability
Support & Reliability
Models degrade silently, so monitoring is non-negotiable. We watch input distributions, output confidence and business outcomes together, and retrain on a defined schedule or when a drift threshold trips.
- Drift, latency and accuracy monitoring with alert thresholds
- Scheduled retraining, shadow deployment and staged rollout
- Human review queue staffing and quality audit of AI-assisted decisions
Documentation & Training
Documentation & Training
AI adoption depends on trust. We document what the model does and does not do, and train business users to read confidence scores, challenge outputs and escalate correctly, plus the technical handover your data team needs to own it.
- Model cards, data lineage and evaluation reports kept current
- Business-user training on interpreting scores and handling exceptions
- Responsible-AI guidance covering escalation, review and record-keeping
Why Nectar?
Why organizations move AI from experiment to operations with Nectar
We start with a measurable business decision, validate whether the data can support it and engineer the monitoring, governance and human review needed for dependable production use.
We start from the decision
Every engagement names the business decision to improve and its current baseline, so accuracy targets mean something commercially.
Honest data-readiness verdict
If the data cannot support the use case we say so in weeks, and propose the data work needed instead of shipping an untrustworthy model.
Models that stay accurate
Drift monitoring, scheduled retraining and human-in-the-loop review keep performance from decaying quietly after go-live.
Cost per prediction, published
We report inference cost alongside accuracy, because a model that improves a metric while doubling run cost has not helped.
Data protection reviewed first
Training data, retention and residency are agreed with your privacy team before a single model is fitted.
Data science that transfers
Your analysts pair with ours on notebooks, pipelines and evaluation, so capability stays with you after the engagement.
Portfolio
Relevant work
Selected engagements where we delivered this capability end to end.
AI/ML
Machine Learning-Based Accounting Software for AM/NS
Auto reconciliation of customers' and vendors' statements using ML.
Read case study
Telecom OSS
NIDC: Intelligent Dunning & Collection System for Telecom
An AI-powered postpaid dunning solution designed for telecom operators to automate revenue recovery and reduce churn.
Read case study
BI Analytics
NPS: App-based Network Performance Management System
Network performance management, KPI monitoring & alarm management system.
Read case studyReady to start your ai solutions initiative?
Book Your Free CallRelated services
Microsoft IoT Solutions
Connect devices, stream their data into Azure and act on it: device management, real-time analytics and predictive maintenance.
Read more
Microsoft Training & Adoption
Role-based training and adoption programs that get your teams using the Microsoft platform you have already paid for.
Read more
Data Management & Analytics
Consolidate scattered data sources, then report on them: Power BI dashboards, data integration and predictive analytics.
Read more