Services

AI consulting services for companies that need implementation, not hype.

DoneGrip helps businesses act on the AI rush with practical support in strategy, developer training, private AI infrastructure, on-prem and local models, RAG, MCP, and AI QA testing.

From AI pressure to a buildable plan

AI Strategy and Enablement

We help leadership and operations teams decide where AI belongs, which use cases are worth building, and how to turn AI urgency into controlled business workflows.

AI roadmap tied to business value, risk, and team capacity
Pilot projects selected for measurable impact, not novelty
Guardrails, review points, and adoption support built in

Hands-on training for software teams

AI Developer Training

We train developers to use AI coding tools, prompts, agents, code review workflows, test generation, and API patterns without lowering engineering standards.

Practical AI playbooks your developers can reuse immediately
Better use of copilots, agents, prompts, and code review loops
Workshops shaped around your stack, repos, and delivery process

More coverage without more manual drag

AI QA Testing and Automation

We use AI to turn requirements, tickets, releases, and product context into QA plans, regression checks, exploratory testing flows, and clearer defect reports.

Broader test coverage from requirements, user stories, and risk areas
Repeatable QA workflows for releases, regressions, and AI features
Human-reviewed defects with reproduction steps and severity notes

On-prem AI, local models, MCP, and RAG

Private AI Infrastructure

We help companies deploy private AI infrastructure: on-premises MCP servers, local and open-weight LLMs, model gateways, RAG, vector search, and secure tool access.

Private model access with data residency, RBAC, and audit logging
MCP servers connected to approved tools, APIs, repos, and data sources
Local retrieval, vector search, and governed agent workflows

Engagements

Start with the AI work most likely to pay off.

Local AI advisory sprint

A focused discovery and pilot plan for companies deciding where AI should be used first.

AI developer training lab

Practical workshops for developers using AI coding tools, agents, prompts, and review workflows.

AI QA automation build

An AI-assisted QA workflow for release planning, regression coverage, exploratory testing, and issue triage.

Private AI infrastructure build

An on-premises MCP, local model, RAG, vector search, and access-control setup for controlled enterprise use.

Need help deciding what to build with AI?

Send the business problem, current tools, data boundaries, and what success would look like. We will recommend a focused AI starting point instead of a generic roadmap.

Start an AI service brief