Code that holds under load. Systems you never rebuild.
We build digital products and embed AI into business operations — from architecture to production. Every project is bespoke engineering with a measurable outcome, not a template with the client’s logo.
We show working systems,
not stock imagery.
The product interfaces below are generated in code and mirror the kinds of systems we build: dashboards, agent orchestration, deploy consoles. On-screen data is illustrative.
An impression fades in a week. A system runs for years.
Every project starts with a process map, not a mockup: what connects to what, which data crosses system boundaries, who owns a failure. We remove manual work wherever a decision can be handed to code — and keep a human wherever the cost of error is high.
The result is not a website or a bot — it’s an operating system for your business that keeps running long after the team has closed out the project.
Numbers over opinions
A system, not a prototype
Accountability after handover
Five disciplines under one
line of accountability
Web engineering, product design, AI integration, data and automation, brand and growth run as a single process — not five separate contracts. One team carries each project end to end, with no context lost between phases.
Web engineering
Product design
AI integration
Data & automation
Brand & growth
Intelligence works inside your operations —
not on top of the interface
Agents, RAG, and copilots connect to your company’s real data and policies — and hold production load, not demo load. Results are measured in hours freed and approval cycles shortened, not in feature counts.
AI agents
Replace manual handling of requests, emails, and tickets: the agent reads the context, acts within its access policy, and escalates only the edge cases.
RAG on your data
The model answers from your internal knowledge bases and policies — no training on third-party data, nothing leaves your perimeter. Every answer traces back to its source document.
Process automation
We wire CRM, email, document workflows, and internal systems into one loop: data moves between services with no manual re-entry.
Copilots
An assistant inside your product or CRM interface: it suggests the next step, drafts the document, pulls up the record — without switching windows.
LLM integrations
We connect models via API to your existing infrastructure: task queues, webhooks, access rights, and logging work exactly as they do in the rest of the system.
Analytics & forecasting
The model finds patterns in operational data and predicts the likely outcome — churn, demand, load — before it shows up as a line in the quarterly report.
From an operator’s request
to a production release
Reconcile June’s incoming invoices against the terms of contract A-1187. I need the discrepancies.
Checking 214 line items against the contract. Estimate: 40 sec.
Found 7 discrepancies: 5 are VAT rounding, 2 exceed the SLA limit. Proposal: close the rounding items automatically, escalate the 2 items to legal.
Agreed. Escalate those, close the rest.
Done. Report filed in CRM, copy sent to the CFO. Manually: 6 hours for 214 items with a team of two. Now: 41 sec at 99.6% accuracy.
Systems running
in production
The four case studies below are the showcase. Full write-ups — architecture, stack, and the hard engineering problems — live in the portfolio.
Unified team operating platform: planning, AI agents, execution control
Planify started as a personal planner and grew into a team operating platform. A Telegram bot and an HTTP API run in a single FastAPI process on Supabase, with an AI layer on top: turning chat threads into tasks, transcribing calls into minutes, a daily planning digest, and background control agents. The system runs in production with a real team and grows through modules the owner switches on as needed.
Digital backbone for a nationwide agro holding: marketplace, CRM, lead generation
For a nationwide supplier of seeds and crop protection products, we built a complex of three connected parts: a public B2B agro marketplace, a product CRM for farmers with a working personal dashboard, and field print materials for an industry trade show. The marketplace runs on real data — profiles, offers, requests, deals, auctions via Supabase Realtime. The project is in active development: some connectors are live, some dashboards are still showcase.
Video infrastructure for a sports chain: from court booking to player highlight reel
We built a video platform that turns court cameras into personal highlight reels for players, on top of the wristband tracking software the club already runs. Separately, we designed a bridge to the external booking CRM with two-way slot synchronization, and moved the chain’s landing page to code-based editing.
Autonomous outbound sales engine for an industrial manufacturer
The AI sales engine deploys on top of the real corporate mailbox of a composite rebar manufacturer’s sales team. It connects over IMAP, reconstructs the correspondence history and the manager’s writing style, mines the mailbox into a client database and links the price list, then drafts reactivation emails and commercial proposals. Sending is protected by a dry-run mode and mandatory manual approval: the drafts are ready, a 3,712-contact database is consolidated, and the first wave awaits the client’s go-ahead.
Five stages between your
brief and production
Each stage closes with a deliverable you accept before we move on. Nothing is handed over verbally: every step ends with a document, a prototype, or working code.
Discovery
Deep dive
We audit your systems, integrations, and access rights, and lock in the success metrics. The output is a technical specification with architecture — not a two-page brief.
Design
Architecture
We design the information architecture, the interface, and the technical schema in parallel. The prototype is built on real data, not demo data.
Build
Development
We build the system to the approved architecture: code, integrations, CI/CD, tests. Progress is visible on a private staging environment.
Launch
Release
We deploy to production with monitoring, load testing, and a rollback plan. We train your team.
Scale
Scaling
We track post-launch metrics and make changes driven by data. The architecture absorbs growing load without a rewrite.
Every number is tied
to a project and a date
Eleven engineers.
Zero intermediaries.
ÆTHER works with a closed circle of clients: no more than six projects in production at a time. Each one is carried by the same trio — a designer, an architecture engineer, and an AI specialist — assigned from brief to release. The project’s technical director is personally accountable.
The format changes.
The discipline doesn’t.
We don’t sell hours and we don’t blur scope. Every model fixes the scope of work, acceptance points, and definition of done before the start.
Fixed scope, timeline, and budget. We ship one product or system — a website, an integration, an AI agent — from brief to production with no parallel streams.
- The solution is approved up front
- Payment in two installments: kickoff and acceptance
- No scope reopening mid-project
An engineering team embeds into your cycle on an ongoing basis — from architecture to operations. Two-week iterations, each closed with a demo.
- For systems that grow with the business
- Milestone-based payment
- Scope reviewed every sprint
Standing engineering capacity without hiring or idle time between projects. We keep the system healthy and drive metrics to their targets.
- Priorities fixed per quarter
- Monthly contract
- Scope reviewed quarterly
What clients ask
before we start
Timelines, cost, and what happens to your data — before you sign the brief. If your question isn’t on the list, ask it directly.
How long does a project take
How is pricing structured
What stack do you work with
How do you deploy AI
What happens after launch
How do you handle confidentiality
The first meeting is
an audit, not a pitch.
45 minutes: we walk through your current process, find the points of manual work, and calculate the payback — before anything is signed. NDA by default. A proposal with the numbers within 5 business days.