01AI practice
Generative AI
Build grounded LLM applications, retrieval workflows and multimodal features with evaluation, permissions and cost controls.
- RAG and knowledge search
- Document intelligence
- Multimodal product features
AI and machine learning services
AI systems engineered around useful work—not demos.
Fly Technovation helps international teams evaluate, build and operate AI capabilities with real data, human oversight, measurable quality and production controls.
Responsible production AIUseful, reviewable and observableIndia · Worldwide
AI practices
Capability across the AI delivery stack.
Model choice is only one part of the system. Useful AI also depends on data, workflow design, evaluation, permissions, human judgment and an operating model.
02AI practice
AI agents
Design tool-using workflows that can plan and execute bounded tasks while keeping approvals, state and exceptions visible.
- Tool and API orchestration
- Human review checkpoints
- Agent evaluation and tracing
03AI practice
Machine learning
Develop predictive capabilities around a defined operational decision, representative data and an explicit measurement method.
- Forecasting and classification
- Recommendation systems
- Anomaly detection
04AI practice
Natural language processing
Turn unstructured language into searchable, classifiable and reviewable information for customer and internal workflows.
- Classification and extraction
- Summarization and search
- Conversational interfaces
05AI practice
Computer vision
Evaluate image and video workflows for detection, inspection, document capture and other visually grounded decisions.
- Object and defect detection
- OCR and document capture
- Image workflow automation
06AI practice
Data science and analytics
Prepare governed data foundations and analysis that support useful models, trusted reporting and measurable experiments.
- Data readiness assessment
- Feature and pipeline design
- Experiment analysis
07AI practice
AI operations and MLOps
Move models beyond notebooks with repeatable deployment, evaluation, monitoring, cost visibility and rollback planning.
- Model delivery pipelines
- Quality and drift monitoring
- Observability and governance
08AI practice
Custom AI integration
Connect appropriate AI services to your permissions, applications and operational systems without hiding failure modes.
- Workflow automation
- Private data integration
- Secure application APIs
09AI practice
AI strategy and readiness
Prioritize opportunities according to business value, data readiness, operational risk and the evidence needed to proceed.
- Opportunity mapping
- Build-versus-buy decisions
- Responsible delivery roadmap
Production before theatre
What makes an AI feature dependable?
A convincing demonstration is not the same as a dependable system. We make operating assumptions and failure paths visible before AI becomes part of important work.
Representative evaluation
Test examples reflect the real information, edge cases and quality expectations of the workflow.
Grounding and validation
Approved sources, structured outputs and deterministic checks constrain what the system may do.
Human oversight
High-impact or uncertain decisions have an explicit review, correction and escalation path.
Operational visibility
Quality, latency, cost, failures and changes remain observable after release.
Delivery pathway
From opportunity to operated AI.
The duration depends on data access, integration and risk. The sequence stays focused on reducing uncertainty before production investment expands.
- 01
Map the work
Define the user, decision, source information, current effort, failure impact and intended business improvement.
- 02
Audit readiness
Review data access, quality, permissions, privacy, integration constraints and the human expertise already involved.
- 03
Prove the risky part
Build the smallest realistic experiment with representative examples and an evaluation method agreed in advance.
- 04
Engineer production controls
Add authentication, review paths, observability, guardrails, cost limits and dependable system integration.
- 05
Measure and improve
Monitor quality, cost, latency, exceptions and business impact; refine or stop according to evidence.
Technology approach
Model-flexible by design.
We select technologies after understanding the workload. These are representative categories—not partnership claims or a requirement to use every tool.
Frontier language modelsOpen-weight modelsEmbedding modelsVector databasesPython ML ecosystemWorkflow orchestrationModel evaluationCloud AI platformsData pipelinesMonitoring and tracing
AI across operations
Begin with the work, not the industry label.
Industry context matters because the cost of error, data sensitivity, review responsibilities and useful measurements differ.
Commerce
Catalog enrichment, support assistance, product discovery and demand analysis.
Finance and operations
Document processing, reconciliation assistance, forecasting and exception review.
Healthcare workflows
Administrative automation and knowledge support with proportionate privacy and human oversight.
Logistics
Demand signals, route and capacity analysis, document intake and operational exception handling.
Manufacturing
Visual inspection, maintenance signals, knowledge retrieval and production reporting.
Professional services
Research support, document intelligence, knowledge search and controlled drafting workflows.
AI-specific questions
Useful answers before an AI investment.
The right technical answer depends on the workflow, data, impact and operating constraints—not the popularity of a model.
Do we need a custom AI model?
Usually not at the beginning. We first test established models, retrieval, deterministic automation and existing services before considering fine-tuning or custom training.
How do you select a model or provider?
We compare the options against the real workload: output quality, privacy terms, latency, integration fit, operating cost and the ability to evaluate failures.
Can AI use our private company information?
Potentially, after data classification, access, provider terms, retention and logging are reviewed. Sensitive information should not enter a workflow until those controls are understood.
How do you reduce hallucinations and unsafe outputs?
The appropriate controls depend on risk. Common measures include retrieval from approved sources, structured output, validation, evaluation sets, permissions, monitoring and human review.
Can you turn an AI prototype into a production system?
Yes. Production work typically adds security, integration, testing, evaluation, observability, cost controls, failure handling and operational ownership that a prototype may deliberately omit.
Can we begin with a small proof of concept?
Yes. A focused POC should answer one decision with representative data and an agreed evidence threshold rather than attempt to become a rushed production application.
Start a useful conversation
Choose one AI opportunity worth proving.
Share the workflow, available information, current effort and cost of error. We’ll recommend a proportionate discovery, experiment or production path.