AI Engineering
Support copilot for Ledgerly (Dummy Name)
An LLM copilot that drafts and routes support replies across 40k monthly tickets.
61%faster first response
4.8★agent satisfaction
Production-ready AI systems, not demos
Generative AI applications, intelligent agents, copilots and custom LLM workflows — engineered for accuracy, security, governance and operating cost, and monitored after launch.
20+AI systems in production
<2s< /strong>Typical response latency
4–10 wksTypical pilot to launch
// WHAT'S INCLUDED
Deliverables under this practice.
✓
AI Consulting Services
Scoping where AI actually creates measurable value in your workflows.
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AI Integration Services
Wiring AI into your existing tools, data and processes.
✓
Generative AI Development
Applications built on top of LLMs for real business tasks.
✓
AI Agent Development
Multi-step agents that take action, not just generate text.
✓
AI Copilot Development
In-product assistants that help your users work faster.
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LLM Development Services
Fine-tuning, prompting and RAG pipelines built for accuracy.
✓
Machine Learning Development
Predictive models and classical ML where it outperforms LLMs.
// HOW WE RUN THIS
Our process for this practice.
01
Discover
Identify the workflow, data and success metric.
02
Prototype
A working pilot against real data within weeks.
03
Harden
Accuracy testing, guardrails and cost tuning.
04
Launch
Phased rollout with human-in-the-loop where needed.
05
Monitor
Ongoing accuracy, cost and drift monitoring.
Tools & technologies
// PROOF
Recent work in this practice.
What it typically costs
AI pilots typically start around $20k and validate value against real data within a few weeks; production agent systems with monitoring and guardrails range $60k–$180k depending on data readiness.
// FAQ
Common questions.
Will this work with our own private data?
Yes — most engagements use retrieval-augmented generation (RAG) over your own data rather than fine-tuning, which is faster and keeps data under your control.
How do you handle hallucination and accuracy?
Through grounding responses in retrieved source data, evaluation test sets, and human review workflows for high-stakes outputs.
What about data governance and security?
Data handling, model choice (including on-prem/private options) and access controls are scoped explicitly before any pilot starts.