AI Strategy
An AI strategy that moves AI out of the lab and into production.
A lot of AI projects stall somewhere between an impressive demo and a system people can count on. We help you close that gap. Our AI strategy moves AI out of the lab, starting with the use cases. We pick the use cases that will actually pay off, build them on your data and your stack, and put the right guardrails in place so they keep working as you scale.
- Use-case ROI mapping
- RAG & LLM apps
- LLMOps
- Responsible AI
What we deliver
Everything between the idea and the SLA
One accountable team covers strategy, data, engineering and operations, so nothing gets lost in a hand-off between consultants and builders.
- Strategy
AI opportunity discovery
Working sessions and process analysis to rank use cases by value, feasibility and risk. You walk away with a prioritized, budgeted roadmap, not just a slide deck.
- Data
Data readiness & pipelines
Source mapping, data quality fixes, vector stores and retrieval pipelines, so your AI answers from your company’s facts instead of the open internet.
- Build
LLM & RAG applications
Copilots, knowledge assistants, document processing and summarization, built on the right model for the job: OpenAI, Anthropic, Gemini, Llama or Mistral.
- Integrate
Enterprise integration
AI connected to Salesforce, SAP, ServiceNow, SharePoint, your ERP and custom apps through secure APIs, right where your people already work.
- Operate
Evaluation & LLMOps
Golden datasets, automated evals, prompt and version management, cost and latency monitoring, and drift alerts from day one.
- Trust
Governance & security
PII redaction, access controls, audit trails and policy guardrails designed around SOC 2, HIPAA, CCPA and the NIST AI Risk Management Framework.
How it works
A path to production in weeks, not quarters
- 01
Weeks 1–2
Discover
We map your processes, data and constraints, then agree on the success metric and the baseline we’ll be measured against.
- 02
Weeks 3–6
Prove
We build a thin but real slice on production data, with evals in place. No throwaway demos. You make the go/no-go call on evidence.
- 03
Weeks 7–12
Launch
We harden security, finish integrations, add monitoring and human-in-the-loop controls, then roll it out to real users.
- 04
Ongoing
Scale & operate
We expand to related use cases and tune cost and quality. Then we either train your team to run it or keep a pod running it for you.
Why DJ Computing.IO
Engineering discipline, applied to AI
We’ve been shipping enterprise software since 2012. AI gets the same rigor: tests, observability, security and a clear owner.
- Model-agnostic architecture, so you’re never locked into one LLM vendor
- An evaluation harness shipped with every release
- ISO 9001:2015 and ISO/IEC 27001 certified delivery
- Your cloud, your data: AWS, Azure or Google Cloud
- Outcome-based pricing available through AI Pods
- Knowledge transfer built into every engagement
FAQ
AI Strategy: FAQs
Can’t find what you’re looking for? Book a call and ask us directly.
For a focused use case, you can usually expect a production pilot in 8 to 12 weeks: two weeks of discovery, a four-week proof on real data, and four to six weeks of hardening and integration. Larger programs run as a series of these increments.
We’re model-agnostic. We build with OpenAI, Anthropic Claude, Google Gemini, Meta Llama and Mistral models, deployed through Azure OpenAI, Amazon Bedrock, Google Vertex AI or self-hosted, depending on your data residency, cost and performance needs.
Yes. Wherever possible, we work inside your own cloud environment. We apply PII redaction and role-based access, use enterprise model endpoints that don’t train on your data, and follow our ISO/IEC 27001 certified security practices.
Every engagement starts with an agreed baseline and success metric, such as handle time, deflection rate, accuracy or cycle time. We ship automated evaluations and dashboards so you can see quality, cost and business impact at any time.
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