Model tuning took weeks, slowing market entry. On Bedrock, updates take days, cutting costs 50% and boosting retention 75% for telecom.
An India-based startup specializing in generative AI agents supports over 120 languages and dialects for enterprise clients in regulated sectors like banking and telecom.
Fine-tuning language models traditionally took weeks, slowing go-to-market speed for price-sensitive regions. The company needed to avoid the high...
“We envisage brands continuing to develop generative AI use cases as the technology becomes more integrated with their IT layers. Companies see a clear ROI from conversational generative AI augmenting customer interactions at every touchpoint.”
GenAI voicebot platform for BFSI and telecommunications sectors.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Manual checks across 160 games bottlenecked support. GenAI now references player data to resolve 90% of inquiries in minutes.
New document types took weeks of manual coding. GenAI cut setup to minutes, letting teams refine extractions via natural language.
Building an agent took 50 people weeks of work. A bank now designs and tests the same tool in just four days.
Local servers caused latency bottlenecks. Cloud AI now translates complex docs in days, cutting months from market entry timelines.
Generic models missed brand tone, forcing manual edits. A RAG workflow injecting style guides cut editing effort 30% and boosted quality.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
Model tuning took weeks, slowing market entry. On Bedrock, updates take days, cutting costs 50% and boosting retention 75% for telecom.
An India-based startup specializing in generative AI agents supports over 120 languages and dialects for enterprise clients in regulated sectors like banking and telecom.
Fine-tuning language models traditionally took weeks, slowing go-to-market speed for price-sensitive regions. The company needed to avoid the high...
“We envisage brands continuing to develop generative AI use cases as the technology becomes more integrated with their IT layers. Companies see a clear ROI from conversational generative AI augmenting customer interactions at every touchpoint.”
GenAI voicebot platform for BFSI and telecommunications sectors.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Manual checks across 160 games bottlenecked support. GenAI now references player data to resolve 90% of inquiries in minutes.
New document types took weeks of manual coding. GenAI cut setup to minutes, letting teams refine extractions via natural language.
Building an agent took 50 people weeks of work. A bank now designs and tests the same tool in just four days.
Local servers caused latency bottlenecks. Cloud AI now translates complex docs in days, cutting months from market entry timelines.
Generic models missed brand tone, forcing manual edits. A RAG workflow injecting style guides cut editing effort 30% and boosted quality.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.