Performance funnel rebuild
A B2B services company needed acquisition it could trust. We fixed tracking first, then scaled Meta and Google against true cost per acquisition.
- Location
- Remote / EU
- Project
- Performance Marketing
- Industry
- B2B Services
LLMsystemsthatstayupinproduction.
Orchestration with LangChain / LangGraph, plus the observability, versioning, and incident patterns that keep AI features reliable.
Syed Wajeeh ul Hassan
Harris Khan
Bassam HashmiWorking with businesses across the US, UK, Europe and Australia
Automate — AI & Engineering
Ship the graph.Operate the system.
We harden prompts, chains, and agents into operable services — with tracing, cost controls, and rollback paths when models or tools change.
- Orchestration patterns with LangChain / LangGraph
- Tracing, cost, and quality monitoring
- Versioning and rollback for prompts and graphs
How we work
Build once.Improve continuously.
- 01
Discovery
We learn what you sell, who buys it and what is working now. No proposal before this conversation.
- 02
Diagnosis
We audit traffic, tracking, funnel and systems, then name the real constraint on growth.
- 03
Plan
A scoped plan with clear deliverables, timelines and the numbers we will be judged on.
- 04
Build & Scale
We execute in the open, report on what matters, fund what is proven and cut what is not.
Case Studies
Work measured by outcomes,not activity reports.
Support automation system
An online retailer needed to scale customer support without adding headcount. We automated triage and drafted replies with brand context built in.
- Location
- Remote / US
- Project
- AI Automation
- Industry
- E-commerce
Development
Conversion site
Digital Marketing
Lifecycle campaigns
AI Automation
Ops automation
Client feedback
Technology is not the objective.Performance is.
“They redesigned our internal workflows with automation and delivered measurable efficiency, without overselling what AI could do.”
Michael Carter
Chief Technology Officer
“Support operations became dramatically more efficient after the conversational system went live. Clear scope, honest reporting.”
Sophia Bennett
Head of Operations
“They tied paid spend to pipeline we could actually trust. The monthly report finally answered whether the marketing made money.”
Daniel Hughes
Director of Growth
FAQ
Questions about MLOps & AIOps
MLOps is the practice of getting machine learning and AI systems into production and keeping them reliable: deployment, monitoring, versioning, evaluation. AIOps in our work means the operations layer for language-model applications specifically: prompts, agents, retrieval pipelines and their costs.
LangChain provides the building blocks for connecting models, tools and data. LangGraph adds controllable, stateful workflows for agents and multi-step pipelines. Both are widely adopted, open source and well documented, so your team is not locked in.
Latency, token cost per request, error rates, output quality scores, and drift when a model or prompt changes. You get dashboards and alerts, not a monthly surprise.
Yes. We audit what is running, add evaluation and observability, then fix the highest-risk gaps first. Most systems can be made dependable without a rebuild.
Model routing sends simple tasks to cheaper models, caching avoids repeated calls, and prompt optimisation reduces token usage. We set a budget and alert before it is reached.
Ready to talk about MLOps & AIOps (LangChain, LangGraph)?
A 30-minute call. We will look at your current setup and tell you where the biggest gap is — whether or not you work with us.