Build 03
AI automation that survives six months in production.
Algoace designs and builds AI workflows, retrieval-augmented generation (RAG) systems, intelligent agents and LLM integrations using n8n, OpenAI, Claude and Pinecone, embedded into the systems you already run: CRM, support, data pipelines and internal tools. It is for operators replacing repetitive manual work with automation that a real team can maintain.
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12 years40+ shipped products6 industriesKarachi and Sharjah
“A demo automation takes an afternoon. A production one takes error handling, retries, human review steps, cost limits and logs you can read at 2 AM. We build the second kind. It costs more than the demo and it is still running a year later.”
Three problems we usually find
The pilot worked and nobody trusts it.
No evaluation set, no accuracy numbers, no review queue. We add an eval harness so you can see, in a table, what it gets right.
The RAG answers are confident and wrong.
Bad chunking, no metadata filters, no citations. We rebuild retrieval and make every answer show its sources.
Costs went up with usage and nobody planned for it.
No caching, the largest model for every call. We route by task, cache aggressively and set spend limits per workflow.
What you get
| Deliverable | What it includes | When you get it |
|---|---|---|
| Automation map | Which processes, in what order, expected savings | 48 hours |
| Working workflows | On your n8n or ours | Weekly |
| Eval harness | Accuracy and cost per run | With the first workflow, before it goes live (weeks 2 to 3) |
| Human review steps | Where the risk requires them | With each workflow, before it goes live |
| Runbooks and monitoring | Error alerts, retry and failure handling, cost-per-run tracking, and runbooks your team can follow | Handover week |
| Support window | 30-day support | Starts at handover |
How we work on this
- 01Scoping call
- 02Automation map
- 03Data and access audit
- 04Build the first workflow end to end with evals
- 05Roll out remaining workflows
- 06Handover and monitoring
Stack and tools
- n8n
- OpenAI
- Claude
- Pinecone
- Supabase
- PostgreSQL
- LangChain
- Python
- Node.js
- Slack
- HubSpot and GoHighLevel integrations
Off-the-shelf AI tool or a custom system
| Criterion | Off-the-shelf tool | Custom system with Algoace |
|---|---|---|
| Time to first result | Days | 2 to 3 weeks for the first workflow |
| Fit to your process | You adapt to the tool | The system adapts to you |
| Data control | Your data in their tenant | Your data in your accounts |
| Cost at scale | Per-seat pricing grows with the team | Per-run cost, optimised by routing and caching |
| Maintainability | Vendor roadmap | Your team, with runbooks, on open tooling (n8n) |
Our recommendation: start with an off-the-shelf tool if the process is generic and you have fewer than five people doing it. Build custom when the process is your competitive advantage or touches customer data.
Proof
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AI automation
Every automation we ship carries an evaluation set and a cost-per-run figure before it goes live.
Who this is for, and who it is not for
Who this is for
- Operations, support and RevOps leaders with a measurable manual process.
- Founders whose product needs an AI feature with real accuracy numbers.
Who this is not for
- "Add AI to our product" with no defined task.
- Projects that require training a model from scratch.
How pricing works
Fixed scope, fixed price, senior people. No hourly rates. The written scope after the call is the quote. See how we work.
Questions founders ask us
Which processes are worth automating first?
The ones that are frequent, rule-based and currently done by someone senior. The automation map ranks them by hours saved against build effort.
n8n or a custom codebase?
n8n for orchestration because your team can read and change it; custom code for the parts n8n cannot express. Both live in your accounts.
How do you measure accuracy?
An evaluation set of real examples, scored before launch and re-scored on every change. You see the numbers.
Which models do you use?
Claude and OpenAI models, chosen per task by accuracy, latency and cost. We route small tasks to small models.
What about our data privacy?
Your data stays in your accounts. We can run on regional endpoints and we document what leaves your environment.
Will it keep working when the model changes?
The eval harness is exactly for that: re-run it when a model updates and see what moved.
Do you also audit AI systems for safety?
Yes, as a separate line: AI safety, evaluation and red teaming.
Related
Send a brief
Same three questions as the call, then your details. You will hear from a senior person, not a bot, within one hour during business hours (Monday to Friday, 9 AM to 6 PM GST) and within twelve hours outside them.
Ready when the work has to be right the first time.
A 30-minute scoping call with a senior engineer, designer or growth lead. You will leave with a written view of scope, risks and a realistic timeline, whether or not you hire us.
12 years40+ shipped products6 industries
Karachi and Sharjah