AI Solutions That Deliver Measurable Business Impact

We build production-grade AI systems that go far beyond experimentation. Our AI engineering teams design end-to-end pipelines: data ingestion, model training, serving infrastructure (REST/gRPC endpoints), and monitoring for model drift and data quality degradation.

Whether you need a custom classification model, a recommendation engine, or an intelligent document processing system, we deliver AI that solves specific, measurable business problems — not just impressive demos.

Before investing in AI development, most organizations need expert guidance: which problems are best solved with AI, what data is required, what's the build vs. buy tradeoff, and how to build internal ML literacy over time.

Teklino's AI consulting engagements are structured as 4–8 week sprints, delivering a prioritized AI roadmap, data readiness assessment, vendor evaluation (OpenAI vs. Anthropic vs. open-source), and a proof-of-concept validation plan with success metrics defined upfront.

Machine learning is most powerful when it's trained on your proprietary data to optimize your specific business objectives. Our ML engineers work with structured tabular data, time series, images, and text — building models that genuinely outperform rule-based systems.

We stress-test every model against production-representative data and edge cases, then package it with a retraining pipeline so model quality improves continuously as new data arrives — not just on launch day.

Generative AI opens entirely new product categories: AI writing assistants, automated report generation, synthetic data creation, creative content tools, and code generation copilots. We build generative AI products using foundation models (GPT-4o, Claude 3.5, Gemini) with carefully engineered prompt chains and retrieval-augmented generation (RAG) architectures.

Our generative AI builds include content safety guardrails, output evaluation frameworks, and latency optimization — because generative AI products need to be reliable and fast, not just impressive.

You don't always need to build AI from scratch — sometimes the fastest path to value is integrating existing AI APIs and models into your product or workflows. We integrate OpenAI, Anthropic, Google Gemini, AWS Bedrock, and specialist AI vendors (Whisper for speech, Stable Diffusion for images) into your existing applications.

Our integration work includes appropriate abstraction layers so you can swap models as the AI landscape evolves, without rewriting your entire application.

Large Language Model development goes beyond API calls — it includes fine-tuning pre-trained models on your domain-specific data, implementing efficient inference with quantization and batching, and building the evaluation harnesses needed to measure model quality systematically.

We fine-tune open-source LLMs (Llama 3, Mistral, Phi-3) for domain adaptation, implement PEFT techniques (LoRA, QLoRA) to reduce training cost by 80%, and deploy models on scalable GPU infrastructure with autoscaling based on request volume.

An AI chatbot built with LLMs and RAG is fundamentally different from the scripted bots of 2018 — it understands natural language, retrieves context from your knowledge base, handles complex multi-turn conversations, and escalates to human agents when needed.

We design and build enterprise AI chatbots for customer support, internal HR/IT helpdesks, and sales qualification — with full conversation analytics, human handoff workflows, and continuous improvement loops.

Turning raw data into actionable business intelligence requires more than dashboards — it requires trustworthy data pipelines, a well-modeled data warehouse, and analytics that answer the questions your leadership team is actually asking.

Teklino's data analytics practice covers the full stack: ELT pipeline setup (dbt + Airbyte), cloud data warehouse implementation (Snowflake, BigQuery, Redshift), semantic layer modeling, and embedded analytics dashboards in your product.