Build AI Employees for Your Business
Build, deploy, and manage intelligent AI agents for support, sales, research, operations, and voice — grounded in your business data and connected to the tools your team already uses.
Built for production AI workflows · API-first · Enterprise-ready architecture
Early access · Founded 14 January 2025 · Mumbai, India
AgentLabz is an early-stage company building its platform. We publish no customer names, usage statistics, or traction figures, because we are not yet in a position to verify them. Every interface shown on this site is a demo workspace with example data.
One console for every AI employee you run
Configure the role, connect knowledge, scope the tools it may call, then watch every run. The preview below uses example data from a demo workspace.
app.agentlabz.tech / overview
Demo WorkspaceSupport Agent
Example Agent · customer support role
- Status
- Ready
- Knowledge Base
- Connected
- Tools
- 6
- Channels
- Chat + API
- Memory
- Enabled
Example workflow
- 1Customer message
- 2Knowledge retrieval
- 3Agent reasoning
- 4Tool execution
- 5Response
- 6Analytics
Interface preview using example data. AgentLabz is in early access — nothing shown here represents real customer usage.
AI Employees Built for Real Work
Each role ships as a starting configuration — instructions, tools, memory settings, and knowledge structure — that you adapt to your own process and data.
Everything You Need to Run AI Agents in Production
Multi-Agent Orchestration
Coordinate specialised agents and workflows with shared context and controlled task delegation.
Knowledge Base
Connect documents and business knowledge so agents can provide grounded, cited responses.
Memory
Session, contextual, and persistent agent memory, enabled per agent where it is appropriate.
Tool Calling
Allow agents to interact with approved business tools and APIs through explicitly scoped permissions.
Workflow Automation
Trigger agents from events, schedules, APIs, and business workflows, with approval gates on writes.
Voice AI
Conversational voice workflows with streaming speech input and output, transcripts, and human transfer.
Analytics & Observability
Monitor agent executions, latency, token usage, cost, errors, and outcomes across every run.
Developer API
REST APIs, webhooks, SDK-ready architecture, and programmatic management of agents and knowledge.
Integrations
Connectors for common business tools, plus a generic API and webhook path for everything else.
Enterprise Security
Authentication, role-based access control, encryption, audit logging, and tenant-isolated data handling.
Where AI employees earn their place
Customer Support
Automate repetitive customer questions while maintaining a clear human escalation path.
Sales
Qualify leads, answer product questions, follow up, and schedule meetings.
Research
Search connected knowledge sources and produce structured, sourced reports.
Operations
Automate repetitive workflows across internal systems with exception handling.
Voice
Build conversational voice agents for inbound and outbound workflows.
Knowledge
Let employees and customers interact with company knowledge in natural language.
Engineering decisions behind the platform
We build the agent runtime, retrieval layer, and serving path ourselves, so isolation, traceability, and cost control are structural rather than bolted on later.
Multi-Tenant by Design
Workspace-level isolation of data, keys, and vector namespaces from the first line of schema.
Accelerator-Ready Serving
An inference layer designed for batching, cache reuse, and GPU-backed model serving.
Real Multi-Agent
Actual delegation between specialised roles, not one prompt pretending to be a team.
Versioned & Reversible
Agents, prompts, and tool schemas are versioned so a change can be evaluated and rolled back.
Security-Focused
RBAC, audit logging, tenant isolation, scoped tools, and encrypted secrets.
Bring Your Own Model
Route to your own provider accounts, private endpoints, or open-weight models.
API-First
Every product capability is reachable through REST, streaming, and webhooks.
Durable Workflows
Replayable pipelines with checkpointing, so a failed run resumes instead of vanishing.
Voice as a Channel
Streaming voice treated as a first-class deployment target, not a bolt-on widget.
Observable by Default
Trace every step, score every run, and attribute cost per execution.
Built for Accelerated AI
Our architecture is designed to take advantage of GPU acceleration for AI inference, embeddings, speech workloads, and high-throughput agent execution.
Sub-second
Voice turn latency target
Design target
Continuous
In-flight request batching
Design target
Cached
KV cache reuse across agent steps
Design target
Autoscaled
Pools segmented by latency class
Design target
Dynamic batching
Incoming requests are grouped in-flight so GPUs stay saturated without holding interactive traffic behind long generations.
KV cache reuse
Shared system prompts and repeated agent context are cached across steps and turns instead of being recomputed each call.
Continuous batching
Finished sequences leave the batch immediately and new ones join, keeping utilisation high under bursty agent workloads.
Streaming inference
Tokens are emitted as they are produced, so the first word reaches the user long before the answer finishes.
Tensor parallelism
Large open-weight models are sharded across multiple GPUs to fit memory and cut per-token latency.
Embedding acceleration
Bulk embedding and cross-encoder re-ranking run on GPU, making large knowledge bases practical to re-index frequently.
What we run today
AgentLabz does not currently operate its own GPU fleet. The inference layer is architected so that model serving, embeddings, and speech workloads can run on GPU-accelerated infrastructure, and deeper integration with accelerated computing platforms is on our technology roadmap.
Technology roadmap includes deeper integration with accelerated computing platforms and optimised GPU inference.
Read the inference architectureCloud-Native Architecture
AgentLabz is designed to run as a scalable cloud SaaS platform: stateless APIs in front, durable background execution behind, and inference isolated on its own scaling path.
Containerised Services
Each platform service ships as an independently deployable container image.
Scalable APIs
Stateless request handling behind a gateway so capacity scales horizontally.
Background Workers
Long-running agent executions and ingestion jobs run outside the request path.
Model Inference Services
Inference isolated behind its own service boundary so serving scales independently.
Databases
Managed relational storage for workspaces, agents, runs, and audit history.
Object Storage
Durable storage for uploaded documents, transcripts, and call recordings.
Vector Search
Embedding indexes with per-tenant namespaces for permission-aware retrieval.
Monitoring
Structured logs, traces, and metrics exported to a central observability pipeline.
Secure Networking
Private networking between services with public exposure limited to the gateway.
Autoscaling
Queue-depth and load-based scaling for both API and worker tiers.
Planned AWS Infrastructure
Not yet deployedAgentLabz does not run on AWS today. The platform is built cloud-native and portable, and AWS is the infrastructure we are planning to build on as we scale.
Scalable compute
Containerised API and worker tiers with autoscaling for bursty agent workloads.
Storage
Object storage for documents, transcripts, recordings, and evaluation artefacts.
Databases
Managed relational and vector storage for workspaces, runs, and embeddings.
AI inference
GPU-backed inference capacity for model serving, embeddings, and speech workloads.
Monitoring
Centralised logging, tracing, and alerting across services.
Production workloads
Multi-environment deployment with isolation between preview and production.
An agent platform vs. a scripted chatbot
A capability comparison against the intent-matching chatbot generation, not against any named vendor.
Built for Businesses Automating Real Work
The platform is designed for teams with repetitive, knowledge-heavy workflows — the work that scales with headcount today.
Industries we are designing for
Agents that act in the systems you already run
Slack
Microsoft Teams
Notion
Google Drive
Dropbox
Salesforce
HubSpot
Gmail
Outlook
Zapier
From empty workspace to a deployed employee
Create Workspace
Invite your team, set roles, and choose your data region.
Choose an AI Employee
Start from a role template — support, sales, research, voice — or define your own.
Connect Knowledge & Tools
Upload documents, connect sources, and scope exactly which tools the agent may call.
Deploy & Observe
Ship to chat, voice, or your own app via API, then watch every run in observability.
Early Access & Product Development
AgentLabz is actively developing its enterprise AI agent platform and validating workflows across support, sales, research, operations, and voice. We work directly with a small number of early-access teams: you get engineering time from the people building the runtime, and we get honest feedback on what production actually demands.
We are a 5-person team in Mumbai. When we have customer results we can verify and publish, they will appear here — until then this page stays free of logos and numbers we have not earned.
Plans that scale with usage
AgentLabz is in early access. Plans and pricing below are our intended commercial structure and may evolve as product development continues.
Starter
$99
per month
For small teams putting a first AI employee into a real workflow.
- 3 AI employees
- Chat and email channels
- Knowledge base with citations
- Tool calling with scoped permissions
- Run history and basic analytics
- Email support
Growth
$499
per month
For teams running agents across support, sales, and operations.
- 25 AI employees
- Voice AI workflows
- Multi-agent orchestration
- All integrations and API access
- Role-based access control
- Full observability and run replay
Enterprise
Custom
annual agreement
For organisations with security, scale, and data-residency requirements.
- Unlimited AI employees
- Custom model routing and private endpoints
- SSO and advanced audit logging
- Data residency and retention controls
- Security review and DPA
- Named technical contact
Questions enterprise buyers ask us
What exactly is an AI employee?+
An AI employee is a configured agent with a role, instructions, memory, scoped tools, and knowledge sources. Unlike a scripted chatbot, it can plan across multiple steps, take approved actions in connected systems, and hand off to a human with full context.
What stage is AgentLabz at?+
AgentLabz was founded on 14 January 2025 and is an early-stage company building its platform. We are in active development and early access, validating workflows across support, sales, research, operations, and voice. We do not publish customer names, usage statistics, or traction figures because we are not yet in a position to verify them.
Are the dashboards shown on this site real customer data?+
No. Every interface shown on this website is a demo workspace with example data, and it is labelled as such. Nothing on this site represents real customer usage.
Which language models do you support?+
The platform is model-agnostic by design and integrates with hosted providers such as OpenAI, Anthropic, and Google, plus open-weight models including Llama-family models. Model selection is per agent, so changing a model does not require rewriting the agent.
Do you train models on our data?+
No. Customer documents, conversations, and configurations are not used to train shared models.
How do you reduce hallucinations?+
Answers are grounded in retrieved passages and cite their sources. Agents can be configured with confidence thresholds so that they abstain and escalate to a human instead of guessing.
Can agents take real actions in our systems?+
Yes, through tool calling against your APIs and our connectors. Every tool is explicitly scoped, and sensitive actions can be configured to require human approval before they execute.
Is AgentLabz SOC 2 or ISO 27001 certified?+
No. We hold no security certifications today and we do not claim otherwise. Our architecture is built with recognised control criteria in mind — encryption, access control, tenant isolation, and audit logging — and formal audit is part of our roadmap as the company matures.
Can we use AgentLabz for healthcare or other regulated workloads?+
The architecture is designed to support sensitive workflows through access control, retention settings, and audit logs, but we are not HIPAA certified and we have no BAA in place today. Regulated deployments need a joint scoping review with your compliance team first.
Where is data stored and how is it isolated?+
Data is encrypted in transit and at rest, and workspaces are logically isolated from one another. Region selection and retention policies are configurable, with specifics agreed contractually for enterprise deployments.
Do you offer a free trial?+
We are running an early-access programme rather than a public self-serve trial. Request access and we will scope a workflow with you.
Can we bring our own model keys?+
Yes — routing to your own provider accounts or private endpoints is supported for enterprise deployments, while the AgentLabz orchestration layer stays the same.
What file types can the knowledge base ingest?+
PDF, Word, PowerPoint, CSV, plain text, and website URLs. Documents are chunked, embedded, indexed, and re-indexed on a schedule you control.
Do you publish an uptime SLA?+
Not yet. We are pre-general-availability, so we do not publish an uptime SLA we could not stand behind. Availability commitments are agreed contractually with enterprise design partners.
How do you measure agent quality?+
Through evaluation runs against a fixed question set, plus per-run observability covering latency, token usage, cost, tool errors, escalation rate, and human review outcomes.
How do you handle API rate limits?+
The API returns standard rate-limit headers with per-key quotas. Enterprise keys can be provisioned with higher limits.
What does onboarding look like?+
A scoping session on one workflow, a knowledge and integration setup sprint, a supervised pilot where a human reviews every response, then a staged expansion of autonomy based on measured accuracy.
How do we contact you?+
Email hello@agentlabz.tech or use the contact form. Enquiries reach the founding team directly — we are a five-person company.
Build Your First AI Employee
Connect your knowledge, define the role, add tools, and deploy an AI agent for your workflow.