Generative AI & NLP
Agam Intelligence designs generative AI and NLP around real work: assistants that answer from your knowledge, classification and extraction on the documents you already process, and conversational experiences that sit inside products - with evaluation and guardrails, not a chatbot demo.
Intelligence
Language systems that hold up outside a demo.
A fluent prototype is easy. A language system that classifies correctly, extracts the right fields, and stays within policy is harder. We start with the task - support, compliance, operations, or a product experience - then choose models, prompts, retrieval, and evaluation so generative AI and NLP change how work gets done.
We build assistants for support, sales, operations, and internal knowledge - grounded in your content, with the tone and limits your brand needs. The product includes logging, feedback, and a path to improve answers after launch.
Tickets, contracts, claims, and forms still arrive as unstructured text. We build classifiers and extractors that turn that mess into structured fields your systems can route, report, and act on - with human review on low-confidence cases.
Chat is only useful if it completes a task. We design conversational flows that authenticate, look up, and hand off to a person - inside your product or channels - rather than an open-ended bot that cannot finish the job.
We treat prompts and models as software: test sets, regression, toxicity and hallucination checks, and red-team cases for the domain. Guardrails are part of delivery so the system fails closed when it does not know.
NLP for the documents you already have - PDFs, emails, tickets, policies - including OCR where needed. The output is structured data and searchable knowledge, not another pile of summaries nobody trusts.
Assistants and classifiers belong in the tools people already open. We integrate with your CRM, ITSM, portal, or product APIs so generative AI is a feature of the workflow - not a separate tab that goes unused.
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Assistants and classifiers are measured on the task - deflection, handling time, extraction accuracy - not how impressive the demo sounded.
Evaluation, access control, and fail-closed behaviour reduce the chance that a fluent wrong answer reaches a customer or a compliance file.
Introduce generative AI into live channels with human handoff and a rollback path, instead of a public launch you cannot reverse.
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