Shipping the system is the start. Someone then has to watch the evals, catch the regressions, control the spend, update the models, and answer at 2am. Managed AI is that someone: a monthly retainer under which we operate your AI systems — built by us or not — with monitoring, evaluation gates, cost telemetry, and daily written progress. Reliability and economics, owned continuously.
Daily
Written progress, every engagement
$47K→$28K
What operated economics look like
100%
Eval gates on production changes
Models drift, providers change behavior, retrieval goes stale, costs creep. Without owned monitoring and evals, the first alert is a user complaint or an invoice. Operated AI fails loudly, early, and to someone whose job it is.
Agencies ship and exit. Internal builders move on. Managed AI takes over systems we did not build: audit first, stabilize, instrument, then operate — with runbooks that outlive any one engineer.
Spend reviews happen once, after a shock. Under management, cost telemetry runs continuously and the monthly report shows cost per workflow, what changed, and what we did about it.
Audit Focus
The first pass is designed to identify the smallest useful intervention: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
The same system, with and without an owner. The right column is the retainer.
| Concern | Unmanaged | Under NavyaAI management |
|---|---|---|
| Quality drift | Discovered by users | Eval suites catch regressions before release |
| Cost creep | Discovered on the invoice | Continuous telemetry, monthly cost report |
| Model updates | Deferred until forced | Evaluated, gated, and shipped on cadence |
| Incidents | Whoever is awake | Runbooks, severities, escalation, postmortems |
| Visibility | Ask an engineer | Daily written progress: changes, blockers, spend |
$47K → $28K
Case study: a Llama 3 70B production workload moved from 4 GPUs to 2 with INT8 quantization, KV-cache pruning, and serving changes — a 42% monthly cost cut with 2.3x throughput.
Read the full auditQualified Intake
A 30-minute consult scopes the runbook: what we monitor, which evals gate changes, what the cost report covers, and the monthly retainer — quoted concretely, before any commitment.
Founder experience — US roles & engagements at
Code and Theory (Stagwell Group)ProsciaWolframHeyNeoTeCSAR Lab · UNC Charlotte
Shipped Work
Products we've engineered and clients we've served — real platforms in production, not slideware.
Construction execution platform for PMC firms — phase-gated workflows, tamper-proof site records, and snag-to-handover tracking that replaces spreadsheets and WhatsApp threads.
Full-stack platform: Next.js, Postgres, async job pipeline, field-team mobile flows.
Secure AI chatbots for HR, support, and sales — RAG-powered answers from company documents with audit trails, aligned to GDPR and SOC 2 expectations.
RAG platform end to end: ingestion, retrieval, guardrails, multi-tenant serving.
SEO, AEO & GEO intelligence engine for developers — triple scoring, Search Console integration, and CLI-first plus MCP agent workflows.
Agent-first product: MCP server, site crawler, scoring engines, WordPress control plane.
AI creative strategist — learns a brand from its URL and generates on-brand ads, video, and copy built to convert, in minutes.
Generative pipeline: brand ingestion to multi-format creative output.
Programmatic SEO platform — generate, preview, and publish thousands of service + location pages in minutes. Trusted by 500+ SEO professionals.
Page-generation engine: templating, bulk preview, and publishing at 50K+ pages scale.
Also trusted by
FAQ
Continuous operation of your production AI: monitoring for quality, latency, and spend; evaluation gates on every change; model and provider updates; incident response with runbooks; and a monthly report covering reliability and cost. Daily written progress comes standard — you always know what changed and what it cost.
Yes — takeovers are half the point. We start with an audit of the architecture, eval coverage, and cost baseline, stabilize what needs it, instrument what is invisible, and then operate. Systems built by other agencies, departed contractors, or internal teams are all fair game.
A monthly retainer scoped at the operations consult, sized to the systems under management — and priced the NavyaAI way: transparently, with the scope itemized, alongside our published cost-plus team pricing. No surprise line items; the retainer covers what the runbook says it covers.
In the practical sense: you get working, monitored, economically-controlled AI without running the operation yourself. The difference from typical AI-as-a-Service is ownership — the systems, models, and data remain yours, in your infrastructure, and you can take operations in-house any time with our runbooks.
Response targets are set per contract by severity, with escalation paths defined in the runbook. Every incident gets a postmortem covering what failed, what we changed, and which eval or alert now catches it.
Continuously — it is half the value. The published $47K to $28K case is what one-time optimization looks like; management keeps the curve down as usage grows, because routing, caching, and capacity decisions are revisited every month with the telemetry in front of us.
