Codexta

The AI that lives your life with you.

One prompt. Entire workflows. Zero app-switching.

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Most AI assistants make you switch apps, repeat yourself, and do the thinking for them. Codexta doesn't.

In a single conversation it can check the weather, conditionally trigger a product search, send a payment, apply for a loan, style a complete outfit, take notes you can recall later, and remember all of it next time — learning your preferences along the way. It's not a chatbot. It's the operating system for your users' daily lives.

C
Codexta Agent
Banking
banking_list_accounts banking_list_transactions + 3 domain tools
Retail
very_search_products very_search_categories + 4 domain tools
Research
brain_search brain_create + 8 domain tools
Development
bash_execute git_clone + 6 domain tools
tools loaded per task · never mixed · zero overlap
01 · Domain Expertise

One agent. Any domain. Zero configuration.

Ask it to find a dress and send £200 to your mother and Codexta recognises two separate domains — splitting the work into isolated task frames, each loading only the tools it needs: retail tools for shopping, banking tools for the transfer.

Banking tools never enter a retail context. Retail tools never touch a payment. Each task frame is isolated and purpose-built — one agent that specialises on demand, without a single line of configuration.

01 · ENTERPRISE KNOWLEDGE

Gigabytes of documents. Correlated in one answer.

This isn't a personal notepad — it's an enterprise knowledge engine. Codexta searches through gigabytes of stored documents using hybrid keyword and vector retrieval, finds the relevant sections across multiple files, extracts the facts, summarises them, and presents the answer with links back to every source.

Ask "What was Very Group profit over the last two years?" — even when 2024 and 2025 live in separate reports — and Codexta locates both, pulls the profit figures, combines them, and cites very-group-2024-report and very-group-2025-report as sources. No single document needs the full answer. The agent assembles it.

ME
knowledge_base
What was Very Group profit over the last 2 years?
C
Codexta 23:04
✓ 3 STEPS COMPLETED
Planning tasks
Correlate profit data across annual reports
Searching brain
Found sections in 2024 + 2025 reports
📊 Very Group · Financial Reports
Profit summary — last 2 years
2024: £142.3m operating profit (Annual Report 2024, p.12)
2025: £158.7m operating profit (Annual Report 2025, p.9)
Combined: £301.0m · +11.5% YoY growth
SOURCES
📎 very-group-2024-report 📎 very-group-2025-report
I've extracted and combined the profit figures from both annual reports.
📁 new-project
Book my usual table if it's free Friday
+ Codexta ⌄
Check calendar & restaurant availability
Condition met — table free at 8pm
Booking confirmed · no apps opened
03 · Autonomy

One prompt. Entire workflows.

One instruction. Codexta checks the conditions, makes the decision, finds what's needed, and moves the money — all before you've finished your coffee. Conditional logic. Cross-domain chaining. Real outcomes.

This is what separates a conversational assistant from a genuine AI agent.

04 · Trust

It never makes things up.

Product prices, availability, account balances, weather — all pulled live from real APIs. If it can't verify it, it won't say it. Your users will trust it, because it earns that trust on every response.

Current balance
£4,182.50
Live · verified from bank API
"probably around £4k" ✕ never guessed
Weather
Always °C, 7-day forecast
SAVED
Banking
Confirm anything over £100
SAVED
🛍
Shopping
Prefer next-day · size M
SAVED
no settings screen · scoped per domain
05 · Memory

Preferences that actually stick — per domain.

Codexta learns how users like to work and saves it — globally, or scoped to a specific domain like weather, banking, or shopping. Preferences persist across sessions and update on request. No settings screen required.

06 · Security

Passwords the model never sees.

When a user signs in, registers, or authenticates, the password never reaches the language model in plain text. It's encrypted the moment it's entered, held in session-only memory, and used at the real value the model never has access to.

Sealed end-to-end — closing an entire class of prompt-injection and credential-leak risk that affects most agent platforms today.

🔒 Credential vault
SEALED
PASSWORD
••••••••••••
EncryptionRSA-OAEP · 2048-bit
Storagesession memory only
Model accessNONE
Runs flawlessly on Gemma 4 — self-hosted, no vendor lock-in
Carefully engineered agent architecture. Efficient enough for open-weight models enterprises can serve themselves.
ENTERPRISE READY
02 · Document Intelligence

Upload a file. Get answers. Build your brain.

Drop in a PDF, Excel spreadsheet, or PowerPoint deck. Codexta reads every page, chunks each section, and indexes it with hybrid BM25 + vector search — instantly queryable, cited back to the exact page and row.

It doesn't just read once. It stores findings in a persistent knowledge base so documents uploaded today answer questions asked next month — no re-uploading, no re-reading, no re-explaining.

PDFvery-group-annual-2025.pdf
XLSpending_payments_q1.xlsx
Read 47 pages · 3 sheets document_read
142 sections indexed brain_create
BM25 + vector index ready hybrid search
"What are the pending payments for Q1?"
brain_search anchor:3 match
↳ pending_payments_q1.xlsx · sheet "Q1 Payments" · rows 3–17
Task orchestration

Each task sees only what it needs to.

Codexta splits every request into isolated task frames. Each frame loads only its domain tools and the exact context that step requires — then passes a single result back. No bloat, no bleed-through, no contamination between domains.

Scenario 01
Product search, basket, finance approval — three domains, one conversation
Find me some Nike Air Max and put it on finance
Found Nike Air Max 90 · size 9 · £129 — added to basket. Checking your credit eligibility now.
Looks good, go ahead
↓ spawns finance task t-1.1 · inheritKeys: basket_total → loan domain
t-1SHOPPING
Find trainers + buy on finance
awaiting loan approval
Context window
🔍very_search → Nike Air Max 90 · £129
🛒very_basket_add → basket BKT-441
💳finance requested · £129.00
waiting for loan ref…
spawn
inheritKeys:
basket_total
t-1.1FINANCE
Apply for credit · £129
approved
Context window
basket_total · £129.00 · from t-1
📋credit_check → eligible
loan_apply → LOAN-7821 approved
🔧tools: banking loaded
handoff
loan_ref
returned to t-1
t-1SHOPPING
Checkout with approved finance
resumed · processing order
Context window
🛒basket BKT-441 · ready
💳basket_total · £129.00
loan_ref: LOAN-7821 · from t-1.1
📦checkout → order ORD-9923 placed
Scenario 02
Two parallel tasks — preferences and cross-task handoff
Find me some shoes. And how do I return items if I'm not happy with them?
↓ splits into 2 tasks · t-27 (shopping) and t-28 (knowledge base) · shoes result inherited by t-28
t-27SHOPPING
Find me some shoes
done · result passed to t-28
Preferences loaded from your profile
male size 8 Nike history ← your profile
Context window
🔍product_search · "shoes men size 8"
👟Nike Air Max · size 8 · £89
🏷SKU: NKE-AM-008 · In stock
inheritKeys
shoes result
→ t-28
t-28KNOWLEDGE BASE
How do I return items?
running · reading return policy
Context window
Nike Air Max size 8 · inherited from t-27
📄doc: returns-policy · loaded
📄doc: returns-process · loaded
Free returns · 28 days · Nike eligible
Task-frame isolation
inheritKeys: parent → child
handoff: child → parent
Context stays lean

Your workflows. One prompt away.

Contact Us Read the docs