For founders & product leaders
Bake AI into your product — without burning runway.
RAG pipelines, LLM features, and agent workflows built end-to-end on OpenAI, Anthropic, or self-hosted models. Production-grade — with evals, guardrails, and cost control.
- 10,000+ hours of LLM pipelines shipped to production
- Stack: OpenAI, Anthropic, Vercel AI SDK, vector DBs, self-hosted
- Evals + guardrails baked in — no shipping hallucinations to prod
The problem
ChatGPT prototypes don't survive contact with users.
Wrapping the OpenAI API is the easy part. Making it accurate, fast, cheap, and safe at scale — that's the part that kills most AI projects.
Prototypes that fall apart at scale
Worked great in the demo, hits 500 users and the latency, costs, or quality crater. No eval pipeline, no observability.
Hallucinations destroying user trust
One confidently-wrong answer in front of a paying customer can undo six months of product work. You need guardrails, not vibes.
API bills exploding once live
Naive prompting costs 5–10× what it should. Without caching, prompt compression, and model routing, you burn margin per call.
Engineering team has no LLM experience
RAG, evals, fine-tuning, function calling — your team knows React. Hiring an AI engineer takes 6 months you don't have.
What we build
AI features that ship to production.
From spec to live feature — with cost modeling, evals, guardrails, and observability so you know it's working.
AI feature scoping & cost model
What to build, what model to use, what it'll cost per request and per month at scale. Written before any code.
RAG pipelines
Vector DBs (Pinecone, pgvector, Qdrant), retrieval, re-ranking, and citation tracking. Built so users can verify answers.
Chat, Q&A & agent workflows
Streaming chat UI, multi-turn memory, tool use, function calling. Built on Vercel AI SDK or custom server-sent events.
Streaming UI
Token-by-token streaming, optimistic UI, retry on failure. Feels instant — even on slow models.
Evals & guardrails
Golden test set, automated regression checks, content filtering, jailbreak detection. No shipping hallucinations to prod.
Observability & cost control
Per-request logging, latency / cost dashboards, prompt caching, model routing. You see exactly what's happening and what it costs.
How we'll work
From idea to live AI feature in 4–6 weeks.
AI scoping (week 1)
Feature spec, model picks, cost model, eval criteria. You know what success looks like before we start building.
Build + eval (weeks 2–4)
Pipelines, prompts, retrieval, UI. Continuous eval against a golden test set so quality is measurable, not vibes.
Ship + monitor (week 4–5)
Production deploy, eval dashboard, cost / latency alerts. 30 days free post-launch tuning included.
Proof
Real AI features, in production.
"We needed real-time call transcription and sentiment scoring. AppsRover built the whole pipeline — Whisper + LLM extraction + sentiment — in 5 weeks. Saved our sales team 20+ hours a week."
Money-back on first sprint
If the first sprint's deliverables aren't useful, you don't pay for them. We take the risk so you don't have to.
Shipped on schedule
Miss a deadline we committed to? You get a 15% credit on that milestone. We plan realistically and hit our dates.
Fixed price, no surprises
Scope is signed before we start. Out-of-scope work is quoted separately — never billed silently.
Your code, your IP
Full source ownership, NDA-friendly, repo handed over from day one. No vendor lock-in.
Book a free consultation
Tell us about your AI use case.
Free 30-min scoping call. Walk away with a model recommendation, cost estimate, and a clear technical path — even if we don't end up working together.
FAQ
Questions, answered straight.
Ready to ship AI that actually works?
Free 30-min scoping call. Walk away with model picks, cost estimates, and a clear technical path.
Book the AI scoping callReplies within 2 hours (7am – 5pm EST). NDA + DPA available before any data shared.