Techvibes

Built to run.Not to demo.

We work inside your company to build AI around how it actually operates — reliable, efficient, state of the art — and we keep it running long after launch day.

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Running at production scale

Figures from the systems we build and operate for our clients today.

  • 18+

    Custom AI projects delivered

  • 55bn

    Tokens processed every month

    About 21,000 a second, on average

    Since you opened this page

  • 80/20

    Open-weight to closed models in production

Selected work

Three systems, built to run

Each one is built around a real operation, measured against the baseline it has to beat and kept running after launch.

01 / 03Brand intelligence for AI search

AI Visibility Platform

Fig. 01 · Answer space across seven AI engines

A platform that measures how brands show up inside ChatGPT, Perplexity, Gemini, Google AI Overviews and other AI engines — and tells teams exactly what to change.

Capabilities

  1. 01Persona-based simulation runs thousands of real buyer conversations across every major AI engine, every day.
  2. 02Proprietary GEO scoring tracks share of voice, sentiment, cited sources and answer variance over time.
  3. 03A recommendations engine turns visibility gaps into prioritised content and technical fixes.

Specification

Client
European marketing & data group
AI engines tracked
7
Enterprise brands monitored
500+
In-house model vs frontier API
2.3× faster
02 / 03Private, fast, low-cost inference

Fine-Tuned Local Models

Fig. 02 · Model weights on your own hardware

We fine-tune compact open-weight models on a client’s own workflows so routine AI work runs on their hardware — faster, cheaper, and without data leaving the building.

Capabilities

  1. 01Task-specific datasets distilled from real workflow traces and expert-reviewed outputs.
  2. 02LoRA fine-tuning and quantisation of 7–14B parameter models for on-prem and edge deployment.
  3. 03An evaluation harness benchmarks every release against frontier-model baselines before it ships.

Specification

Client
Regulated financial-services operator
Faster responses
6×
Inference cost
−82%
Records sent to third-party APIs
0

Illustrative figures

03 / 03AI-native systems that maintain themselves

Software Factories

Fig. 03 · The improvement loop, run by agents

Autonomous engineering systems that watch production, find what’s broken or slow, open their own pull requests and resolve customer support — with humans approving what ships.

Capabilities

  1. 01Agents monitor logs, metrics and user feedback, and turn signals into scoped engineering tasks.
  2. 02Every fix ships as a reviewed pull request with tests — no silent changes to production.
  3. 03Support agents resolve tickets end-to-end and feed recurring issues back into the improvement loop.

Specification

Client
B2B SaaS platform, 40k+ users
Agent-authored PRs per month
140+
Support tickets resolved autonomously
71%
Median bug report to fix PR
<2h

Illustrative figures

Services

What we build, and what we advise on

Engineering is most of the work. Advice comes from having shipped it ourselves.

Build04

  • 01

    White-Label AI Platforms

    We build the engine; clients ship it as their own product. The model layer, integration surface, and infrastructure are ours to handle — what they get is a production-ready AI platform without two years of R&D.

  • 02

    Agentic Systems

    Purpose-built AI agents that work through complex tasks end-to-end — pulling data, making decisions, taking action across tools and systems. Control points are designed in from the start, not retrofitted.

  • 03

    Workflow Integration

    We wire AI into existing stacks and processes. No migration projects, no six-month runways — we find the leverage points in what you already have and ship from there.

  • 04

    Local AI Deployment

    Private and on-premise model hosting for organizations that can’t send data to a third-party API — whether that’s compliance, data sovereignty, or latency. You keep full ownership of the data layer.

Advise02

  • 05

    AI Visibility & GEO

    How you appear in ChatGPT, Perplexity, and Google’s AI Overviews is now a real business problem. We’ve built the methodology and tooling to measure it, optimize for it, and track it over time.

  • 06

    AI Strategy

    Before any code gets written, we help organizations figure out where AI actually fits — what’s worth building, what’s not, and what the path to production looks like.

How we work

From first map to a system that runs

Four stages, each ending in a result you can judge before the next one starts.

  1. 011–2 weeks

    Map

    We find where AI creates real leverage in your business — and where it doesn’t. You leave with a ranked shortlist and a path to production.

  2. 022–4 weeks

    Prove

    A working prototype on your real data, measured against the baseline it has to beat. No slideware.

  3. 036–12 weeks

    Build

    Production engineering: integrations, evaluation, guardrails, observability. Shipped into your stack, not next to it.

  4. 04Ongoing

    Operate

    Systems are monitored and improved continuously — increasingly by the systems themselves.

After launch, you choose

  • A

    We operate it

    We stay on as your AI operations team: monitoring, evaluation and continuous improvement, reported back to you every month.

  • B

    We hand it over

    Your team takes the keys: full source, documentation, runbooks and training. We stay reachable while they settle in.

Stack

The tools we trust in production

Open-weight first: 80% of the model traffic we run goes through open models, with closed frontier models where they earn their place.

  • 01

    Agent Frameworks & Orchestration

    • Multi-agent orchestration systems
    • Claude Code
    • Model Context Protocol (MCP)
    • Custom prompt engineering frameworks
    • Agentic skill libraries & persona encoding
    • Software factory pipelines — ticket→PR automation
    • Model routing & harness-level model selection
  • 02

    Retrieval & Knowledge Systems

    • Retrieval-Augmented Generation (RAG)
    • Semantic embedding pipelines
    • Vector similarity scoring
    • Structured knowledge extraction
    • Context engineering
    • Document intelligence & raw-to-structured ingestion
    • Agent memory architectures (vector + graph hybrid)
  • 03

    Proprietary AI Intelligence

    • GEO scoring algorithms
    • AI response variance analysis
    • Persona-based conversation simulation
    • Cross-platform chatbot analytics
    • Brand mention tracking across AI search
    • Confidence-calibrated decision scoring
  • 04

    Evaluation & Observability

    • OpenTelemetry/OpenInference agent instrumentation
    • Trace analysis & failure-mode taxonomy
    • LLM-as-judge rubric design
    • Golden dataset curation & judge-human agreement scoring
    • Regression eval pipelines
    • Production signal detection → automated fix generation
    • PostHog integration & product analytics automation
  • 05

    Agent Security & Governance

    • Zero-credential sandbox architecture
    • Progressive capability disclosure
    • MCP Gateway access control
    • Cross-application authorization (XAA) via SSO/OAuth
    • Operator-only merge/deploy gates
    • Blast-radius minimization design
  • 06

    Decision & Verification Models

    • Typed decision primitives (Choice/Score/Noul-class)
    • Non-generative verifier integration (Jev-class System One models)
    • Extract → verify → escalate cascades
    • Confidence-gated autonomy
  • 07

    Model Customization & Open-Source AI

    • Open-weight model fine-tuning (GLM, Qwen, Kimi, Llama, DeepSeek)
    • On-prem / local LLM deployment (Ollama, vLLM, llama.cpp)
    • Model distillation & quantization
    • Hybrid local/frontier routing
    • Self-hosted inference cost optimization
    • Open-weight model evaluation & benchmarking against frontier models
  • 08

    Systems & Languages

    • Rust
    • Python
    • TypeScript/Node.js
    • Virtual machine & container architecture (VMs, Docker, microVM sandboxing)
  • 09

    Developer Tooling & Coding Agents

    • Cursor
    • Claude Code / Codex CLI
    • Pi (minimalist, BYOK, local-model-first coding agent)
    • Local/offline coding agent deployment (Ollama-backed)
    • Multi-model coding harness design
  • 10

    Frontend & Application Layer

    • Next.js
    • React
    • Vercel
    • Three.js
    • Tailwind CSS
    • Responsive SaaS UI/UX
    • Agent-facing review/approval interfaces
  • 11

    Cloud & DevOps

    • AWS
    • Vercel CI/CD & edge functions
    • Serverless-first architecture
    • Containerized agent sandboxes
    • MCP Gateway infrastructure
    • Virtual machine provisioning & orchestration
  • 12

    APIs & Integration

    • REST API design
    • Webhook orchestration
    • Third-party API integration
    • OAuth & authentication flows
    • MCP server integration
    • Agentic commerce integrations
    • PostHog event/webhook automation

Writing

Notes from production

What we learn building and running AI systems for real operations.

All writing

Which part of your businessshould run itself?

Every company has work that eats hours each week: support queues, reporting, onboarding, reconciliations. Name yours in a sentence. We’ll come back with whether AI can run it and what the first step looks like.

Opens an email to business@trytechvibes.dev