Cognitive RAG Engine
Retrieves, verifies, and grounds responses in trusted enterprise knowledge.
Conversational AI & Intelligent Agents
AI that thinks,
Agents that act
We build enterprise-grade conversational AI systems—from RAG-powered assistants to autonomous agents—that integrate with your core metadata and workflows.
Agent Instance: AU-942
SYSTEM LIVE
LATENCY
42ms
RECALL
99.8%
TOKENS/S
110
Active Reasoning
01
RAG
02
TOOL
"Consulting internal knowledge graph for user account AU-01... Cross-referencing current credit limit with transaction ID 942-X."
System Output
Action executed: AccountLimit.update() success.
✦ Trusted by large and small businesses worldwide ✦
Our Services
What we build
Custom dialogue systems engineered for specific high-value utility, not generic chat.
Natural Language Understanding (NLU)
he engine that translates human speech or text into machine-readable data by identifying the user's Intent (what they want) and Entities (the specific details)
Autonomous Agents
Intelligent agents that plan, decide, and execute actions across APIs.
Enterprise Integration
Connects seamlessly with CRMs, ERPs, and databases for real-time workflows.
Adaptive Personalization
Dynamically adapts responses using behavioral signals and user history.
Omnichannel Delivery
Delivers consistent AI experiences across web, mobile, and voice.
Our Process
Our process
A rigorous, evidence-led engineering cycle from discovery to global deployment.
- 01
Knowledge Audit
We inventory your technical docs and data silos to define the grounding truth.
- You decide
- Data access
- We ship
- Data map
- Artifacts
- Information Arch
- 02
Orchestration
Mapping conversational architectures and task resolution pathways.
- You decide
- Agent personas
- We ship
- Prompt chains
- Artifacts
- Dialogue flows
- 03
Engineering & RAG
Developing RAG pipelines and tool-use capabilities using top models.
- You decide
- LLM provider
- We ship
- Functional agent V1
- Artifacts
- API endpoints
- 04
Deployment
Global rollout with continuous evaluation and safe human handoff.
- You decide
- Release schedule
- We ship
- Production dashboard
- Artifacts
- Accuracy logs
AI Native
Built as Production-Grade AI Systems
We engineer conversational systems that are grounded, controllable, and observable in real-world environments not experimental prototypes.
- Grounded responses using verified data sources
- Controlled tool execution across internal systems
- Real-time orchestration with low-latency pipelines
- Continuous evaluation for accuracy and reliability
Retrieval RAG
Zero-hallucination grounding in your actual documents.
Tool Calling
Direct integration with internal APIs for real-time actions.
Evaluations
Systematic testing of AI accuracy before every release.
Guardrails
Semantic blocks to prevent off-topic or unsafe interactions.
Technology Stack
Stack and platform layer
We design our stack around real-world constraints—latency, reliability, and scalability.
Dialogue
Reasoning models optimized for speed.
GPT-4oClaude 3.5Llama 3
Orchestration
Systems for managing multi-step workflows.
LangChainLlamaIndexNode.jsPython
Vector Data
Infrastructure for fast, precise grounding.
PineconeWeaviateSupabasePostgreSQL
Voice & Interfaces
Real-time delivery layers across channels.
TwilioElevenLabsVercel
Case Studies
Case studies
Evidence-based solutions built for our clients.
Featured · Enterprise AI
Knowledge-Grounded Retrieval System for Global Logistics
Challenge: Managing 50,000+ technical documents across silos leading to massive delays in operator support.
Intervention: Custom RAG orchestration using Pinecone and Claude 3.5, with sub-second retrieval across 12 legacy data sources.
- Outcome: 98% accuracy in technical response matching and a 40% reduction in average handling time.
- 98% Recall Accuracy
Architecture note: Multi-modal vector indexing with automated metadata extraction and semantic guardrails.
RAGEnterprise AIVector SearchLLMOps
SaaS Ops
Autonomous Billing & Support Agent
Challenge:
What we shipped:
Fully automated 85% of tier-1 support tickets with direct API-driven task resolution.
85% Ticket Resolution
Customer CX
Global Multilingual Voice Assistant
Challenge:
What we shipped:
Real-time voice dialogue in 12 languages with native-level latency and accent handling.
12+ Languages SupportedQuestions
Frequently Asked Questions
Engineering and business questions regarding AI agent deployments.
We use Retrieval-Augmented Generation (RAG) which forces the model to only answer based on provided documents, combined with semantic guardrails that block off-topic or uncertain outputs.
Yes. Our agents are 'tool-use' capable, meaning they can be given strictly-defined access to your APIs to execute real-world tasks securely.
We implement secure architectures—often using enterprise API agreements or private cloud deployments—ensuring your data is never used to train public models.
We typically deliver a functional 'V1' prototype in 4 weeks, with full production rollout including enterprise integrations in 8-12 weeks.