Now shippingSee it →
Brainnova
ENPT-BR
Consulting + products

We build AI-native
systems that think,
act, and ship.

We build agentic workflows, LLM-powered products, and cloud-scale AI platforms — and advise the teams building their own. Everything we ship is AI-native from day one, whether we're delivering it or sitting next to your engineers.

Now shippingLinya — The AI co-writer for novelists.See the product →
Book a discovery callSee what we build

Multi-agent systems · RAG pipelines · LLM orchestration · AI-native apps · Agentic workflows

Shipped
5+
AI systems in prod
Clouds
3
AWS · GCP · Vercel
Live product
1
Linya at linya.ai
live orchestration6 agents active
PLANNERCODERREVIEWERDEPLOYERMEMORYTOOLSORCHESTRATOR
msgs/s1,284 ▲ 12.4%
Stack we use
ANTHROPICOPENAILANGGRAPHAWSGCPVERCELSUPABASEPOSTGRESTYPESCRIPTPYTHONANTHROPICOPENAILANGGRAPHAWSGCPVERCELSUPABASEPOSTGRESTYPESCRIPTPYTHON
Products we ship

Our launched
product.

Linya is live — the AI co-writer we built for novelists.

Chapter 7 · The ConfrontationMemory loaded

Aria stepped into the throne room. Torchlight moved across the obsidian walls — the same walls her mother had warned her about, when she was seven and still believed warnings were for other people.

ContinueRewriteExpandAdd dialogue

Continuity warning · critical

Aria's eyes are blue. Established in Ch.3 and Ch.5 — not green.

Launched

Linya

AI co-writer for novelists · linya.ai

Continuity-aware. Character-aware. World-aware. The first AI built for novelists who actually finish their books — it has already read yours.

  • ✓Story Memory embeds every chapter, character, world note, and upload
  • ✓Continuity Engine versions facts and flags plot holes before readers do
  • ✓Brainstorm threads that send the good ideas straight into a chapter
Visit linya.ai
The problem

Most AI projects never
leave the notebook.

Impressive demos, hallucinating agents, cloud bills that surprise the CFO. The gap between a GPT wrapper and a production-grade AI-native system is the gap we work in.

AI-native, not AI-wrapped

We don't bolt LLMs onto legacy code. Every system is designed agentic-first — reasoning, planning, and acting as a first-class architectural concern.

LLM ops from day one

Prompt versioning, token budgets, eval suites, hallucination guards, and cost dashboards baked in — not bolted on after the first incident.

Shipped, not prototyped

Every agentic system we deliver runs under real traffic with rollback, runbooks, and evals. Clean code, written docs — no lock-in to us.

The outcome

AI in production.

4–16

Week engagements

Typical build: a few weeks to a few months, depending on scope.

5+

Systems shipped

LLM-powered products, agent pipelines, and production platforms.

1

Launched product

Linya — an AI co-writer for novelists, live at linya.ai.

live orchestrationbuild in progress
PLANNERCODERREVIEWERDEPLOYERMEMORYTOOLSORCHESTRATOR
PLANNER → spec the nav
CODER → nav.tsx ✓
PLANNER → spec the catalog
CODER → filters wired
CODER → grid rendered
REVIEWER → low contrast on filter
CODER → fix shipped
DEPLOYER → deployed ✓
tokens/s18,420 ◆ stable
https://yourproduct.app
buildinglive
Shipped
prompt → production~6s demo · 2–4 wks real
Who builds it

Our team are AI-native developers — fluent in agentic workflows, model orchestration, and the full toolchain.

The same people design it and ship it.

How a real RAG pipeline runs

Chat with a PDF.
Watch the pipeline.

Five scenes, real round-trips. From the moment a user sends a message until the citations land in their chat: rate limit, query expansion, hybrid retrieval, generation, attribution, claim grounding, atomic persist. The participants on stage change with each phase — this is what production agentic AI actually looks like.

live RAG pipeline

section-4.pdf · 24 pages

● 01 · Inbound● 02 · Expand + Embed● 03 · Retrieve + Rerank● 04 · Generate + Verify● 05 · Persist + Audit
APPAPIREDISDBAPILLMEMBEDDERAPIDBRERANKERAPILLMAPPAPILLMDBWORKERS
APP→API: POST /messages · section 4?
API→REDIS: check rate limit
REDIS→API: 992 tokens left
API→DB: INSERT user_message
API→LLM: expand query
LLM→API: 4 paraphrases
API→EMBEDDER: embed × 4
EMBEDDER→API: 1536-dim × 4
API→DB: ANN (pgvector) × 4
API→DB: BM25 (tsvector) × 4
DB→API: 200 hits · RRF fused
API→RERANKER: rerank top-5 · BGE
RERANKER→API: top-5 ranked
API→DB: expand parents ±2
API→LLM: chat_stream(prompt)
LLM→API: token deltas...
API→APP: ASSISTANT_MESSAGE_DELTA × N
API→LLM: verify · piggyback
LLM→API: attribution weights
API→LLM: ground 3 claims
LLM→API: 3 claims · 100% grounded
API→DB: INSERT + audit_log · COMMIT
API→WORKERS: dispatch LOO refinement
chat
⚡ preparing⚡ retrieving⚡ generating⚡ verifying
what does section 4 say?
✓ 5 chunks found · recall@5 0.92
Financial institutions must maintain transaction records for at least 5 years [1] and provide them to regulators upon written request within 30 days [2].
attribution[1] 60%[2] 40%
✍ 3 atomic claims · 100% grounded
◆ saliency: high · high
|
complete
  • retrieval_ms342
  • llm_ms1,840
  • total_ms2,212
  • assurancehigh
How we compare

Brainnova vs. the
alternatives.

Agencies, freelancers, and a new hire each have tradeoffs. Here's how a small team compares.

Brainnova

Competition

  • Multi-agent expertise

    ✓
    ✕

  • Time to first prod ship

    2 – 4 wks
    2 – 3 mos

  • Cloud architecture

    ✓
    DEP

  • Senior engineers only

    ✓
    ✕

  • Clean handover

    ✓
    ✕

  • Ongoing partnership

    ✓
    ✓

Have a project in mind?

Book a 30-min intro
What we build & advise on

Full-stack AI.
Build or advise.

Agentic systems need more than a good model. They need a solid app underneath, the cloud to run it reliably, and senior engineering judgement throughout — we deliver all four, end-to-end or alongside your team.

01

Agentic AI Systems

Multi-agent workflows, LLM orchestration, and RAG pipelines — designed to reason, plan, use tools, and act autonomously at scale.

  • ✓Multi-agent orchestration & agentic loops
  • ✓RAG, vector search, long-term memory
  • ✓LLMOps: evals, traces, cost & safety guardrails
02

AI-Native Software

LLM-powered apps, APIs, and data pipelines built AI-first — not retrofitted. Production-grade from day one, owned by your team.

  • ✓AI-native apps with Next.js, TypeScript, Python
  • ✓Generative UI & streaming LLM interfaces
  • ✓Internal AI copilots & developer platforms
03

AI Cloud Infrastructure

Scalable cloud platforms tuned for AI workloads — GPU pipelines, vector stores, inference APIs — observed and cost-controlled end-to-end.

  • ✓AI-optimized infra on AWS, GCP, Vercel
  • ✓Vector databases, embeddings pipelines, inference
  • ✓LLMOps observability, SLOs, cost guardrails
04

Consulting & Advisory

Senior engineering judgement for teams building their own — software architecture, AI strategy, and code-level reviews. We sit next to your engineers when you don't need us to deliver.

  • ✓Architecture audits & second opinions
  • ✓AI strategy, build-vs-buy & roadmap reviews
  • ✓Software engineering & AI development reviews
05

AI Training for Companies

Hands-on workshops and structured curricula that bring your engineers up to speed on AI coding tools — Claude Code, Cursor, GitHub Copilot, Codex, Gemini — and the production patterns around them.

  • ✓Workshops on Claude Code, Cursor, Copilot, Codex & Gemini
  • ✓RAG, evals & LLMOps bootcamps tailored to your stack
  • ✓Curriculum tuned to your team's tools, level & codebase

Want to talk about a build?

Talk to an engineer
Who we work with

Built for engineers.
Accessible to operators.

For technical teams

What technical teams get.

  • ✓Eval suites from day one, not after launch
  • ✓LLM tracing, token budgets & cost dashboards your CFO can read
  • ✓Hallucination guards, retries, rate limits, fallbacks — out of the box
For founders & operators

What founders and operators get.

  • ✓Fixed-scope sprints. Weekly demos of working AI. No surprises.
  • ✓One Slack channel, one PM, one tech lead.
  • ✓Outcomes tied to business metrics, not model benchmarks
Common questions

What we get
asked first

How much does an engagement cost?

Most engagements land between $30k and $250k depending on scope. Discovery is typically $8–15k for a one-week diagnostic with a written architecture and budget. We share a fixed fee before any build starts — no T&M surprises.

What does "AI-native" actually mean?

Agentic reasoning is a first-class architectural concern, not an LLM bolted onto a CRUD app. Prompt versioning, eval suites, traces, cost guardrails and hallucination grounding live alongside your application code from day one.

Who owns the IP and the code?

You do. Code, prompts, evals, infra-as-code, runbooks, and recorded walkthroughs are delivered to your repos under a standard work-for-hire clause. No lock-in to us, no third-party platform you cannot leave.

Which models, clouds, and stacks do you work with?

Anthropic Claude, OpenAI, and open-weights (Llama, Qwen, Mistral) on AWS, GCP, or Vercel. Vector stores: pgvector, Pinecone, Qdrant. Application stack: Next.js / TypeScript / Python. We pick what fits your team, not what we already know.

Do you sign NDAs and DPAs? What about EU data residency?

Yes to both. We sign standard mutual NDAs before discovery and DPAs before contract. EU data residency on AWS Frankfurt / GCP eu-west is supported on every engagement; SOC 2 Type II workflows on request.

What does post-launch support look like?

Optional retainer ($8–20k/month) covers LLMOps, model upgrades, eval maintenance, cost tuning, and on-call for the first 90 days post-launch. Or you take it over completely — your team has the runbooks and we are one Slack message away.

Pick a time

Book a 30-min intro call

A short call to see if we can help. If we can't, we'll say so.

Prefer to send notes instead? Scroll to the contact form

Start a project

Ready to build your
AI-native product?

Tell us a little about what you're building. We reply within one business day.

@
◷
Response time
Within 1 business day
◐
Engagement length
4 – 16 week sprints
What do you need?

We reply within 1 business day.

Let's talkBook a call