Flywheel Your AI Ambitions With Us
OpenTeQ is an AI-first company offering best-in-class AI services — from Generative AI and Agentic AI to RAG, Mixture of Agents, Multimodal AI, and custom model fine-tuning — built on our exclusive OQ Rigor.
Everything under our AI practice.
The first six are techniques. The last three are the reason the first six are still working a year later. Most programs use two or three together.
Generative AI
Language models, grounded in your systems.
Copilots, agents, and retrieval systems built on your own content — with the evaluation, permissions, and cost controls that decide whether they survive past pilot.
- Enterprise copilots and assistants
- Agentic and multi-step workflows
- Retrieval and knowledge systems
- Document intelligence and extraction
- Evaluation harnesses and guardrails
- LLMOps, observability, and cost control
AI Strategy & Advisory
Fewer ideas, properly funded.
We score candidate use cases on value, feasibility, and risk — and tell you which ones we would not build.
- Readiness and data assessment
- Use case scoring and portfolio
- Business case with baselines
Machine Learning & Predictive Analytics
The quiet workhorse behind most AI value.
Forecasting, churn, pricing, risk scoring, anomaly detection, predictive maintenance. Less discussed than generative AI and frequently the better answer — cheaper to run, easier to explain, and simpler to defend to a regulator.
- Demand and revenue forecasting
- Churn, propensity, and segmentation
- Risk, credit, and fraud scoring
- Anomaly and outlier detection
- Predictive maintenance models
- Recommendation and personalization
Intelligent Process Automation
Rules where rules work, models where they don't.
RPA handles the deterministic steps and models handle judgement. Most real processes need both, and the split is where the design work is.
- Robotic process automation
- Model-assisted decision steps
- Exception routing and handling
AI Agents
Work completed, not just described.
Agents that carry a task through several systems and escalate what they can't resolve. Scoped narrowly, instrumented heavily.
- Multi-step workflow agents
- Tool and system integration
- Human approval and escalation paths
Computer Vision
Inspection and recognition at line speed.
Defect detection, damage assessment, safety monitoring, and document imaging where a camera already exists.
- Visual inspection and defect detection
- Damage and condition assessment
- OCR and image-based extraction
Conversational AI
Resolution, not deflection.
Chat and voice agents that finish the routine contacts and hand off cleanly when they shouldn't be handling them.
- Customer service chat and voice
- CRM and ticketing integration
- Handoff and escalation design
Data Foundation for AI
Where most pilots quietly die.
Very few organizations call their data AI-ready. We build the pipelines, permissions, and lineage a specific use case depends on.
- Pipelines and feature engineering
- Permission-aware data access
- Lineage, quality, and cataloguing
MLOps & AI Platform
A model in a notebook isn't a system.
Deployment, retraining, monitoring, and rollback — so the model that performed in March still performs in November.
- Deployment and versioning pipelines
- Drift monitoring and retraining
- Cost and performance observability
AI Governance & Assurance
Answerable when someone asks how it decided.
Model inventories, testing, and audit trails — built during delivery rather than assembled under pressure before a review.
- Model inventory and risk classification
- Bias, accuracy, and drift testing
- Audit trails and explainability
Have a process in mind but not a technique?
Describe what it costs you in time, errors, or headcount. We'll tell you which approach fits, roughly what it takes to build — and when the honest answer is that AI isn't the right tool.
Where AI actually pays in your sector.
The techniques are universal. The regulated decision, the tolerable error rate, and the first build worth funding are not. Select a sector.
Insurance
Two different AI problems sit side by side: document-heavy intake, which suits language models, and pricing and reserving, which suits classical models a regulator can follow.
Claims intake and triage
Extraction and classification from FNOL documents, photos, and notes.
Fraud and leakage detection
Anomaly models over claim patterns that rules-based checks miss.
Underwriting decision support
Risk scoring surfaced inside the underwriter's existing tools.
Policy and coverage assistants
Grounded answers on wordings, with the clause cited.
Document classification at first notice of loss, measured against straight-through processing rate.
Measured before built, monitored after shipped.
AI programs fail on governance far more often than on modelling. These are the terms we work under, and they're worth asking any AI vendor to match.
A baseline before a build
We measure what the process costs today before writing code, because a model with no baseline can never be shown to have worked.
The simplest technique that clears the bar
If a rules engine or a regression solves it, we will say so — even when it makes for a less impressive proposal.
Your data does not train anyone's model
Deployment patterns and vendor terms are selected so your content stays yours, with the configuration documented at kickoff for your security team.
A person approves consequential decisions
Where an output affects a customer, a payment, or a clinical outcome, a human signs off and the audit trail records who.
- Practitioners
- 500+
- Delivery locations
- US, India, Canada
- Deployment
- Your cloud, your tenancy, your controls
- Technique selection
- Chosen per problem, not per trend