From prompt to a real AI product
We build the AI product you ship — custom LLM apps, AI features inside your existing app, and inference APIs. Evals, guardrails, and cost controls go in from day one, so it holds up when real users hit it, not just in the demo. You get a deployed product, not a notebook.
- RAG, evals, and guardrails
- OpenAI, Claude, and open-source
- Streaming, source-cited answers
- You own 100% of the code
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A demo impresses. A product holds up.
We build the parts that decide whether real users trust it — grounding, evals, guardrails, and cost controls — so it works past the first click, not just in the pitch.
Built to perform
The fundamentals we get right on every build — so what we ship keeps working long after launch.
Ships to production
We build the AI product you actually launch, not a demo that breaks the first time a real user does something unexpected.
Grounded in your data
RAG pipelines with hybrid retrieval and citations, so the model answers from your knowledge instead of inventing plausible fiction.
Evals and guardrails
Regression evals, LLM-as-a-judge checks, and guardrails from day one, so quality is measured, not hoped for.
Fast and cost-aware
Streaming responses, multi-model routing, and caching tuned so the product stays fast and the token bill stays sane.
Built for teams shipping AI, not just talking about it
If you have an AI product to launch — or a feature your users will actually touch — this is the team that gets it into production.
Startups & founders
Ship a fundable AI product with grounding, evals, and cost controls built in from v1.
SaaS product teams
Add AI features your users touch — search, drafting, copilots — without a rewrite or a research detour.
Engineering leaders
Bring in senior AI engineers who ship production LLM systems, not a proof-of-concept that stalls.
AI-native teams
Get RAG, evals, and multi-model routing done right so you scale on quality per dollar, not guesswork.
End-to-end, done for you
Custom AI SaaS
We build brand-new AI products end to end — the app, the model layer, the data pipelines, and the infra behind them, deployed and ready for paying users.
AI features your users touch
Add customer-facing AI to an existing app: search, drafting, summarization, copilots, or classification, wired into your data and shipped behind a feature flag.
RAG pipelines
Ingestion, chunking, embeddings, and hybrid retrieval with reranking and citations, so your model answers from your knowledge, not its guesswork.
AI APIs and microservices
Clean, versioned inference APIs and MCP servers your team and customers can build on, with rate limits, auth, and streaming built in.
Evals, guardrails, and observability
Benchmark datasets, automated eval suites in CI, and live dashboards for quality, cost, latency, and drift, so you always know it's working.
Multi-model routing and cost control
A router that picks the right model per request across OpenAI, Anthropic, and open-source, with fallbacks, caching, and usage metering wired in.
An AI product your users trust on sight
Streaming answers, source citations, and a fast, native-feeling UI — the product looks and behaves like something worth paying for.
Streaming answers
Token-by-token responses stream in as they're generated, so it feels fast instead of stuck behind a spinner.
Cited, not invented
Every answer references a real source from your data, so users can trace and trust what the model says.
Cost-aware at scale
Multi-model routing and caching keep responses quick and the token bill predictable as usage grows.
Built on the AI stack you'd choose anyway
We build with the frontier models, frameworks, and infra your team already trusts — no proprietary black box.
What's included
Everything it takes to run an AI product in production — grounded, measured, and cost-controlled, not a prompt in a notebook.
A clear path from idea to live
No black boxes. You always know what's shipping, when, and why.
Discovery and scope
A fixed-scope sprint where we map the use case, audit your data, and prove the approach, so you commit to a plan and price, not an open-ended research budget.
- Map the use case
- Audit your data
- Prove the approach
Architect and prototype
We design the model layer, retrieval, and data flow, then build a working prototype against your real data to prove quality before we scale it.
- Model & retrieval design
- Prototype on real data
- Quality baseline
Build and evaluate
Senior engineers build the product in weekly sprints, with eval suites and guardrails growing alongside the code, not bolted on at the end.
- Weekly build sprints
- Eval suites in CI
- Guardrails wired in
Ship to production
We deploy with streaming UIs, multi-model routing, observability, and rollback paths, then put it in front of your first real users.
- Streaming UI deploy
- Observability & rollback
- First real users
Optimize and scale
We tune cost and latency, expand evals, add models and features, and keep the product getting smarter and cheaper to run month after month.
- Tune cost & latency
- Expand evals
- Add models & features
What you can count on
Fixed-scope AI discovery sprint
Eval, cost, and latency monitoring
You own the code, prompts, and IP
Sources and APIs wired into retrieval
Production AI is where the value is won
Anyone can get a demo working in an afternoon. We build the parts that decide whether real users trust it, keep using it, and don't blow up your token bill.
Hybrid retrieval
Semantic plus keyword search with reranking and metadata filtering, so the right context reaches the model instead of the nearest keyword match.
Evals as a gate
Regression suites and LLM-as-a-judge checks run in CI, so a prompt or model change can't quietly break quality in production.
Guardrails
PII redaction, citation injection, faithfulness scoring, and audit logs, so outputs stay grounded, safe, and traceable.
Streaming UIs
Token-by-token rendering and optimistic UI, so cited answers stream in as they're generated instead of landing after a long spinner.
Multi-model routing
Requests routed across OpenAI, Claude, and open-source by cost, latency, and quality, with automatic fallback when a provider fails.
Usage metering and billing
Per-token and per-seat metering, usage caps, and cost dashboards, so you can bill your own customers and never get surprised by an invoice.
How we compare to the alternatives
There are cheaper ways to bolt a model onto your app. Here's how they hold up once real users, real data, and a real token bill are involved.
| What matters | DIY / API wrapper | Freelancer | Typical AI agency | seotools.pro |
|---|---|---|---|---|
| Grounding and accuracy | Raw prompts, hallucinations and all | A basic RAG demo on sample data | Grounding, if you scope it separately | Hybrid retrieval with reranking and citations, grounded in your data. |
| Evals and quality | You eyeball a few outputs | Manual spot-checks, no suite | Evals promised, delivered late | Automated eval suites and LLM-as-a-judge checks gating every release. |
| Cost and latency | One model, whatever it costs | Single provider, no fallback | Optimized as a later phase | Multi-model routing, caching, and streaming tuned for cost and speed. |
| Production readiness | Works in the demo, breaks in prod | No monitoring or rollback | A separate hardening engagement | Observability, guardrails, and rollback paths wired in from day one. |
| Usage and billing | No metering, mystery bills | Not their problem | Rarely offered at all | Per-token metering, usage caps, and billing hooks for your customers. |
| Code and model ownership | Yours, minus the wrapper's limits | Depends on the contract | Sometimes locked to their platform | 100% yours — your repos, prompts, models, and cloud, no lock-in. |
Anatolii Ulitovskyi
Founder of seotools.pro · in digital marketing since 2008
AI engineers who ship products, not prototypes
seotools.pro is a founder-led studio. The engineers who scope your AI product are the ones who build, evaluate, and ship it — no research theater, no offshore relay.
Product engineers, not researchers
Production from day one
Model-agnostic by design
Cost is an engineering problem
You own everything
Every engagement includes
Questions, answered
Grounded, not guessing
Every answer is retrieved from your data and traceable to a source.
Measured, not hoped for
Evals and guardrails prove quality before and after every release.
Yours to keep
Your code, prompts, models, and IP ship to your accounts with no lock-in.
Ready to ship a real AI product?
Tell us what you want to build. We'll map the fastest path from prompt to a product that holds up in production.
No obligation · honest fit assessment · led by seotools.pro founder Anatolii Ulitovskyi