Custom LLM applications, RAG systems, fine-tuned models, and AI features embedded in your product. Everything is built for reliability, latency, cost control, observability, and fallback handling under real user conditions.
AI agents that plan and execute approved multi-step workflows using defined tools, permission boundaries, and human escalation paths.
Custom NLP models for intent recognition, entity extraction, classification, and sentiment — plus conversational AI systems that resolve, not just respond.
Predictive models, classification systems, recommendation engines, forecasting models, and computer vision workflows built around clean data, measurable accuracy, and production monitoring.
Every AI system we ship is held to the same production baseline — regardless of model or framework:
Benchmarks, test sets, and success metrics defined before any model is trained or integrated
p50/p95 latency targets agreed upfront; every deployment tested against them
Model failure modes are mapped for every engagement. Hallucination risks are evaluated for LLM-based systems.
Escalation paths, confidence thresholds, and override mechanisms built in, not added later.
Token cost, compute cost, and per-request economics are estimated and monitored from day one
Privacy-safe request logging with redaction, access controls, retention rules, latency dashboards, and error alerting.
Your team is trained on prompting, evaluation, and model management before handoff.
Yes. Our machine learning development services cover classification models, prediction systems, recommendation engines, forecasting models, anomaly detection, and computer vision workflows. We support data preparation, feature engineering, model training, validation, deployment, monitoring, and retraining workflows based on business and performance requirements.
Yes. We assess your data quality, structure, access controls, privacy requirements, and integration environment before recommending the right AI approach. Depending on the use case, we may use RAG, fine-tuning, supervised ML, embeddings, rules, or API-based model integration to make your internal data usable for AI workflows.
Off-the-shelf tools are useful for generic tasks, but they often fall short when workflows require domain-specific data, system integrations, approval controls, auditability, and measurable performance. As an AI development company, we build custom AI systems around your business processes, data environment, risk tolerance, and production requirements.
Yes. Many enterprise use cases combine generative AI with AI agents. The LLM handles language understanding or generation, while the agent coordinates tools, APIs, databases, and workflow steps. We design these systems with permission controls, human escalation, monitoring, cost tracking, and failure handling from the start.
We define success metrics before development begins. These may include answer accuracy, retrieval quality, task completion rate, latency, cost per request, escalation rate, hallucination rate, and user acceptance criteria. For ML systems, we also evaluate model-specific metrics, such as precision, recall, F1 score, and prediction error, where applicable.
Yes. We can provide post-launch monitoring, model updates, prompt versioning, evaluation dataset updates, retraining support, workflow optimization, and cost governance. We also document the system and train your team so they can manage core operations independently or continue with managed support.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
