The Zero-Hallucination Guarantee
Your production AI system hits agreed accuracy and cost targets in 90 days — or we keep building for free until it does.
We've shipped 12+ production AI systems. For DigitalAlign (US enterprise), we built a RAG pipeline replacing keyword search across 50,000+ documents — customer support went from 40% irrelevant results to 99.9% retrieval accuracy at 200ms P95 latency, cutting agent handle time 45%. For Bambo AI (UK character AI platform), we rebuilt their LLM selection: 4.7/5 persona consistency, 72% cost reduction, 18% retention lift. Seven engagements total. We have never invoked this guarantee.
The Full Offer Stack
- → Free 30-min architecture audit before you pay anything. Written findings are yours regardless.
- → Week 1: Baseline your system — token costs, P99 latency, hallucination rate. Specific targets agreed in writing.
- → Nishaant directly on your system. Named in the contract. Not handed off after the sales call.
- → Retrieval accuracy target set on Day 1 (e.g., ">99% or we don't stop")
- → Inference cost ceiling agreed upfront — if we can't hit it, we're accountable
- → Weekly 5-min Loom every Friday. No status meetings.
- → Load tested at 10x expected traffic before handover
- → Full runbook + monitoring dashboards. Your team operates it from Day 91.
- → 30-day post-launch support included.
Pricing & Investment
Fixed price agreed after the technical audit — not a time-and-materials estimate that grows. We won't quote until we've seen your stack.
→ A senior AI engineer in the US costs $150–200K/year and takes 3–6 months to ramp up.
→ A US AI agency charges $150–300K for the same scope with a 6-month timeline and no guarantee.
→ We deliver in 90 days, fixed price, with one.
What Happens If We Fail
If we don't hit the accuracy and cost targets agreed on Day 1, you don't pay the final milestone (typically 30% of the total engagement value). We keep working until the targets are met — at our cost, not yours.
What We Build
Production RAG Systems
Retrieval-augmented generation pipelines with real retrieval accuracy, freshness strategies, and observability. Not just a demo.
LLM-Backed APIs
High-throughput APIs wrapping LLMs with caching, routing, fallbacks, and cost controls. Built for production SLAs.
Agentic Platforms
Multi-agent systems with reliable tool use, state management, and human-in-the-loop checkpoints.
AI Observability Infrastructure
ClickHouse-backed logging and metrics for token costs, latency distributions, and accuracy drift.
Vector Search Infrastructure
Production vector databases with hybrid search, re-ranking, and sub-50ms P99 at scale.
Model Serving Infrastructure
vLLM / TensorRT-LLM on Kubernetes with autoscaling, spot instance support, and cost-per-token optimization.
Who This Is For
- → Technical founders who built a working prototype and need it to survive real users
- → CTOs at Series A–C companies where the AI backend is the bottleneck
- → Engineering teams that shipped an MVP but lost control of cost and latency
- → Non-technical founders with budget and a clear product vision, needing full technical ownership
How It Works
Technical Audit (Week 1)
We baseline your current system: architecture, query patterns, cost breakdown, failure modes. You get a written roadmap with specific targets.
Build (Weeks 2–8)
We own the architecture and implementation. Weekly check-ins. You can see every decision in the codebase. No black boxes.
Handover + Runbook
We ship working infrastructure and hand over documentation your team can actually use. On-call is yours from day one — we train you on it.
FAQ
What's your minimum engagement size?
Our engagements typically start at $30,000. We work best with companies that need serious production infrastructure, not quick demos.
How long does prototype-to-production take?
Typically 6–12 weeks depending on scope. Most clients are in production within 8 weeks of the technical audit.
Do you work with non-technical founders?
Yes. We handle full technical ownership during the engagement and hand over a working system with documentation.
What AI stack do you use?
vLLM or TensorRT-LLM for inference, ClickHouse for observability, Kubernetes for orchestration, and whichever LLM fits the workload — Claude, GPT-4o, DeepSeek, Llama.
Can you take over an existing codebase?
Yes. We've rescued several AI products that hit production walls. The technical audit identifies what to keep, what to rewrite, and what to throw out.
All Offers at a Glance
| Service | Offer | Pricing | Proof | Guarantee Trigger |
|---|---|---|---|---|
| AI Product Development | The Zero-Hallucination Guarantee | $35–75K | 72% LLM cost reduction; 12+ shipped | Final milestone withheld until accuracy + cost targets met |
| Backend Engineering | The Zero 3AM Guarantee | $30–55K | 310K inserts/sec; 64% cost save; 12ms P99 | Final milestone withheld until P99 + throughput targets met |
| Technical Product Studio | The Technical Co-Founder Sprint | $45–85K | Lunagen MVP; full ownership model | Final milestone withheld until product operates independently |
| ClickHouse Consulting | The $47K Fix | $25–60K | 40+ deployments; $47K→$8.2K; 0 data loss | Final milestone withheld until cost + latency targets met |
| MVP to Production | Demo to Done Guarantee | $30–60K | 42% cost reduction; 93% reliability gain | Final milestone withheld until production readiness criteria met |
| Data Platform Modernization | Minutes to Milliseconds Guarantee | $35–65K | 187K msg/sec; 0 data loss; 200M events/day | Final milestone withheld until query time targets met |
Ready to Build?
Tell us what you're working on. We'll review it and tell you honestly if we can help — and what it would take.
Schedule a Call