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ivntechlabs

Cost-Optimized AI SaaS & Solutions

Enterprise-grade multi-tenant AI systems, grounded document intelligence, and brand-aligned brand tone control – built with 90% hosting savings in mind.

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Illustrative example

Document answer example

Choose a prepared question

1. Example excerpt

Selected excerpt

An order API can validate an incoming order, store its fields, and return a record identifier.

Example source: api-example.md

2. Prepared answer

In this example, an order endpoint validates the payload before creating a record. The response includes its identifier so the caller can reference it later.

Ready

Prepared sample documents and answers; no search or model request is made.

What we build

Module // 01● Overview

Multi-tenant AI architectures

Host thousands of custom client AI experiences on shared base infrastructure. We use hot-swappable adapter layers and serverless GPU scaling to reduce your operating costs by up to 90%.

Module // 02● Overview

Retrieval-augmented generation (RAG)

Chatbots and internal search engines that answer directly from your PDFs, SOPs, and tickets. Absolute data accuracy with source citations, hybrid semantic keyword queries, and zero hallucination risk.

Module // 03● Overview

Brand-aligned fine-tuning

Adjust models to speak your industry’s vocabulary, adopt a specific persona, and respect strict compliance formats. Standardize output schemas for seamless internal system intake.

Module // 04● Overview

Enterprise data isolation

True zero-trust client security. Complete compartmentalization of corporate data using per-tenant vector database namespaces and rate-limiting to prevent any cross-tenant data leaks.

Feature walkthrough
Illustrative exampleReady

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How a project runs

01
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// Stage 01 //

Discovery

One to two weeks. We map the highest-impact use cases, baseline your current workflows, and scope a low-risk, proof-of-concept prototype you can evaluate in a month.

02
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// Stage 02 //

Build

Two to six weeks to a working pilot. We build clean data pipelines, set up semantic chunking, and run evaluations against real usage to tune accuracy and lower token latency.

03
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// Stage 03 //

Handoff

Production rollout with client dashboards for cost, rate-limiting, and tenant isolation, backed by clean code and long-term support plans.

Example project flow
Illustrative exampleReady

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Common questions

Common questions
Can I afford this as a small or mid-sized company?
Yes. By using shared base models and hot-swappable adapter layers, we keep monthly GPU and API costs exceptionally low. We scope a narrow, high-ROI use case first so the savings are immediate.
What does it cost?
Pilots start at $15k for a single-use-case copilot or document search. A multi-workflow deployment with custom integrations typically lands between $40k and $80k. Monthly operating cost depends on team size and usage volume, but rarely exceeds $1k for teams under 50.
How do you ensure our data remains 100% private?
We support three options: enterprise API tiers with data processing agreements that forbid model training, regional European/US cloud instances, or fully self-hosted open-source models on on-premise hardware.
How long until we see value?
Thirty days to a working pilot your team uses daily. Ninety days to measurable time savings – we baseline the manual process in discovery so the improvement is quantified, not felt. If week four does not show a team actively using it, we redesign before building further.
How do you pick the right model for our scale?
We benchmark task complexity and query volume. High-reasoning tasks use advanced frontier models; extraction runs on cheaper, faster classification models. We show you the math to optimize token usage.
// Prepared answer
Example answer walkthrough
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Source: the answers on this page

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