The full agentic AI
stack. Vertically integrated.
Most agentic platforms are integrations of someone else's ASR, someone else's LLM, someone else's TTS, with a workflow engine on top. SandLogic builds the entire stack — from the silicon that runs the model to the agent that talks to your customer. One vendor. One runtime. One bill.
Perception to silicon, engineered together.
Every layer of an agentic AI workload — listening, thinking, acting, speaking, monitoring, running, computing — is a SandLogic product. They are designed to work together, not glued together. The result: lower latency, lower cost, lower failure rate.
Hear the user. Streaming ASR at sub-300ms latency, code-switching native, 13.27% WER on English long-form. Two engines (transformer + Samba state-space) under one API.
Speak back naturally. Neural TTS with controllable prosody, voice cloning from 30 seconds of consented audio, sub-200ms first-byte latency. 84+ studio voices.
Think. A continuum of language models — from curated open-source (Lexicons), to fine-tuned bridges (Nexons), to in-house Shakti (100M to 30B). Pick the smallest model that meets the bar.
Act. Multi-agent orchestration for outbound sales, inbound service, debt collection, and compliance. Six agent archetypes deployed across telephony, chat, and web.
Compose. Multi-agent chains, adaptive RAG with semantic memory, MCP-native enterprise tools, six vertical templates. The control plane for agent workflows.
Trust. Real-time hallucination detection across four metrics (faithfulness, relevancy, harmfulness, contextual fit), prompt refinement, and confidence-routed human-in-the-loop.
Run. Hardware-agnostic inference. +73% throughput vs vLLM on L40s. 193 model architectures. One binary across cloud, on-prem, and edge.
Compute. Custom AI accelerator IP — Apex (M4096) to Lite (M64). Native INT4/INT8/FP8. The atoms underneath the bits.
Most agentic platforms don't ship the stack.
They ship a workflow engine and call it agentic. The actual intelligence is rented from third-party APIs, with the customer holding the bag on token bills, vendor risk, and data egress. Four reasons SandLogic decided not to play that game.
Agents that share one runtime
Most "agentic platforms" stitch together third-party ASR, third-party TTS, third-party LLM APIs, and third-party guardrails. Every hop adds latency, cost, and a vendor to renegotiate with. SandLogic ships them as one stack with one runtime.
On-prem from day one
Cloud-based agentic platforms can't enter regulated industries without compromises. SandLogic's agentic stack runs air-gapped on customer infrastructure — BFSI, healthcare, telecom, public sector — without sacrificing capability.
Predictable economics
Token-metered APIs make agent costs unbounded. Per-call costs on multi-agent workflows can spiral with longer reasoning chains. SandLogic's on-prem deployment converts inference from variable OpEx to fixed CapEx.
Vernacular by default
22 Indic + 40 foreign languages, code-switching native. Most agentic platforms are English-first and degrade on non-English audio. SandLogic was built for code-switched Indian call-center reality from day one.
Agentic AI already running.
Multi-agent workloads break token budgets.
A single agent answering a question is cheap. Five agents reasoning in a chain — each emitting tokens, each pulling context, each calling tools — can be ten times the cost of a single LLM call. Most enterprises only discover this when the cloud bill arrives.
SandLogic's full-stack approach addresses agentic cost at every layer: smaller in-house models (Shakti / Nexons), an efficient runtime (EdgeMatrix), real-time hallucination filtering (HaluMon), and on-prem deployment that converts variable OpEx to fixed CapEx.
The full token-economy thesis