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.
Enterprise RAG Sandbox Vector Search
Select Sample Query:
1. Vector Similarity Match Match: 94.2%
"ivntechlabs designs custom REST/GraphQL endpoints for complex workflows..."
Source: [contracts_spec.md] 2. Citations & Streamed Response
Yes, ivntechlabs builds custom API endpoints...
State: Active Agent Session No data leaks to public models
What we build
MODULE // 01
● COMPILED
Multi-tenant AI architectures
// Integrity: 100% Secure
// Audit: COMPILED
MODULE // 02
● OPERATIONAL
Retrieval-augmented generation (RAG)
// Integrity: 100% Secure
// Audit: OPERATIONAL
MODULE // 03
● OPTIMIZED
Brand-aligned fine-tuning
// Integrity: 100% Secure
// Audit: OPTIMIZED
MODULE // 04
● VERIFIED
Enterprise data isolation
// Integrity: 100% Secure
// Audit: VERIFIED
READY
How a project runs
01
● WAITING Discovery
02
● WAITING Build
03
● WAITING Handoff
pipeline_trace.log
// pipeline runner online Common questions
// SELECT_QUERY // SYSTEM_FAQ
[q01] 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.
[q02] 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.
[q03] 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.
[q04] 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.
[q05] 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.
// AGENTIC_RESOLVER_OUTPUT //
faq_resolver.sh
AWAITING_QUERY
Source: local_faq_corpus.db Secure Local Connection
Ready to bring AI into your team?
Tell us about the team and the problem. We respond within 24 hours.