Embed every conversation with richer context
We plan data strategy, train or fine-tune embedding models, and wire evaluation plus governance to keep your chatbots confident, safe, and on-brand across channels.
Vector embedding systems we deliver
From model selection to post-deployment drift monitoring, we give your team a reference architecture for embeddings that stay precise, unbiased, and secure.
- • Strategy for dense, sparse, and hybrid embeddings tied to CX or agent KPIs
- • Data cleaning, chunking, labeling, and vector store layouts with retention controls
- • Evaluation harnesses tracking recall, toxicity, bias, and multilingual fidelity
- • Observability playbooks covering drift alerts, re-training, and governance sign-offs
Domain-tuned models
Fine-tune open-source or managed embedding models for legal, fintech, healthcare, or retail vocabularies.
Evaluation & drift lab
Golden datasets, automated scorecards, and guardrail tests for hallucinations or stale embeddings.
Pipelines & feature stores
Automated ingestion from CRM, knowledge bases, and transcripts with lineage into feature stores.
Security & compliance
PII scrubbing, access segmentation, and approval workflows aligned to SOC 2, HIPAA, or GDPR.
Where better embeddings transform chatbots
Omnichannel support memory
Fuse email, chat, voice, and ticket data into embeddings that keep agents context-aware within seconds.
Product discovery & search
Blend semantic embeddings with filters, price, or availability signals for storefronts and marketplaces.
Voice of customer mining
Vectorize surveys, reviews, and NPS verbatims to catch emerging themes or escalation triggers.
Agent assist & compliance
Feed compliant snippets, disclosures, and rebuttals directly into AI agents for regulated industries.
Knowledge base modernization
Chunk legacy PDFs, LMS content, and SOPs into embeddings aligned to your escalation taxonomy.
Ecosystem & tooling
We plug embedding pipelines into the vector stores, orchestration layers, and observability stacks already present in your contact center.
Delivery approach
A collaborative playbook that takes embeddings from ideation to measurable CX lift.
1
Discovery & KPIs: Align on intents, guardrails, success metrics, and channels where embeddings will power responses.
2
Data curation & policy: Connect CRMs, ticketing, and knowledge bases, then apply chunking, labeling, and approval workflows.
3
Training & benchmarking: Select or fine-tune models, run evaluation harnesses, and calibrate multilingual / multimodal behavior.
4
Orchestration & UX: Wire embeddings into RAG flows, agent assist surfaces, and proactive suggestions with governance gates.
5
Monitoring & retraining: Watch drift, bias, and freshness signals; schedule re-ingestion and communicate releases to operations teams.
Build better embeddings for your chatbots
Unlock faster resolutions and safer automation with embedding models, pipelines, and evaluators designed for your customers.
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