Engagement Model

AI Systems Built Around Your Business — Not Subscriptions.

Avigrah is not a SaaS subscription. It is a structured intelligence system tailored to your data complexity, business workflows, and deployment model.

Every deployment is custom. We scope it with you — not for you.

PRICING STRATEGY

Why our pricing is structured this way

AI is not software. It is infrastructure built around your business.

Unlike traditional SaaS subscriptions that charge per seat, custom AI deployments represent permanent capital improvements to your operations.

01

Level of intelligence needed

Complex reasoning cycles and deep planning logic.

02

Data complexity

Volume, diversity, and format of source records.

03

Number of systems

Total custom agents running concurrently.

04

Integrations

API connection depth and live feed pipelines.

05

Scale of deployment

Total environments and query frequency.

What’s included

A typical engagement covers

System Design & Architecture

We map your business workflows, data sources, and outcomes to a tailored multi-agent architecture.

Data Integration & Pipeline Setup

Connect warehouses, third-party tools, and live feeds into the unified Data Layer.

Agent & Intelligence Deployment

Instantiate pre-built and custom intelligence systems scoped to your business outcomes.

Ongoing Optimization & Support

Continuous monitoring, policy enforcement, and intelligence refinement across your organization.

Discuss Your Use Case
No commitment required.We’ll tell you if this is not a fit.

What powers your system

What your investment covers

No hidden costs. Every layer below is scoped to your deployment — usage-based where variable, fixed where predictable.

AI Task Execution

Per-agent reasoning cycles, LLM calls, and inference runs across your deployed systems.

Vector DB Storage

Persistent embeddings and semantic memory for your intelligence layer.

Cache & Context Storage

Fast retrieval layers for agent working memory and session state.

Pipeline Compute

Data ingestion, transformation, and routing across your integrated systems.

Embedding Generation

Document, query, and event embeddings powering retrieval and reasoning.

Projects

Multiple isolated deployments from a single template — each scoped to a team or business unit.

RBAC & Access Control

Fine-grained role-based permissions for operators, analysts, and admins across every project.

Team Seats

Named users across all active projects, with role assignment and audit tracking.

Audit & Traceability Logs

Full decision lineage — every agent action, policy applied, and outcome recorded.

Best suited for

Is this the right fit?

Avigrah is built for organizations that have outgrown point-solution automation and need coordinated, governed intelligence at scale.

Companies with fragmented data systems that need a unified intelligence layer
Teams scaling beyond dashboards and manual reporting
Organizations deploying AI across multiple business units with governance requirements
Enterprises replacing point-solution automation with coordinated, multi-agent execution

Engagement paths

Typical Engagement Structures

Every organization starts at a different maturity level. Here is how teams typically engage with Avigrah.

★ Recommended Pilot

Foundation Deployment

For teams starting structured AI adoption

  • AI Template = multi-agentic system scoped on the basis of problem
  • 1–2 core use cases
  • Data pipeline setup
  • Basic governance layer

Outcome

Your first working intelligence system

Multi-System Rollout

For orgs scaling across teams

  • Multiple projects
  • Shared data layer
  • Cross-agent orchestration
  • RBAC + governance

Outcome

Connected intelligence across functions

Enterprise Intelligence Layer

Full org-wide deployment

  • Custom architecture
  • Multi-team environments
  • Deep integrations
  • Full audit + control system

Outcome

AI becomes operational infrastructure

Scale of engagement

Typical engagements range from mid five-figures to enterprise-scale contracts.

Final scope depends on data complexity, number of intelligence systems deployed, integration depth, and team size. We scope every engagement through a discovery call — no guesswork, no sticker shock.

Next step

Ready to discuss your use case?

We’ll walk through your architecture, scope the engagement, and give you a clear picture of what Avigrah looks like for your business. No commitment required, and we’ll tell you if this is not a fit.

Limited availability each week — slots fill quickly.

FAQ

Frequently Asked Questions

Everything you need to know about custom AI system pricing, integration, and deployment costs.

Business AI costs depend on deployment scope rather than flat subscription fees. Tailored system deployments range from mid-five figures to enterprise contracts. Since AI is operational infrastructure rather than simple software, the cost is tied directly to system complexity and deployment scale.

AI pricing is influenced by five core variables: the level of intelligence/reasoning required, data complexity (volume and format of source records), the number of concurrent systems deployed, the depth of third-party integrations, and the scale of execution (run cycles and usage density).

Fixed pricing does not scale with custom enterprise requirements. Every enterprise operates with unique data pipelines, security guardrails, and system designs. A tailormade deployment is structured specifically to resolve your problems, ensuring you only invest in compute and integrations that directly deliver business outcomes.

The cost of custom AI systems is determined by the engineering design phase, integration architecture, and the computing run-time cache. Custom systems represent a long-term capital improvement that eliminates manual workflow overhead, yielding high ROI compared to off-the-shelf SaaS.

AI automation is priced using a combination of predictable baseline infrastructure (fixed setup and dedicated hosting compute) and variable usage (compute tasks, database calls, and embedding cycles). This structure aligns your costs directly with operational output.