Your starting point

Start with the situation your team is in.

Some teams need a standardized harness rollout and training. Others need shared rules or better results from agents they already use. We adapt the work to your starting point across strategy, process, and platform.

Discuss your next step See how we work

Build useful foundations

No AI rollout yet

Choose and roll out a shared agent harness, training, integrations, and guardrails.

We select a standardized agent harness together, roll it out across the team, and provide the training, MCP integrations, guardrails, skills, and tooling needed to use it well.

What the first step can include

Strategy: Agree your AI goals, choose a shared agent harness, and plan the rollout together.
Process: Train the team, agree shared practices and guardrails, and keep review and decisions with people.
Platform: Roll out the harness with the required MCP integrations, reusable skills, tooling, and access controls.

Related foundations case

Shared configurations, training, and verification

Shared agent configurations, practical training, and verification helped three teams work consistently with AI.

Discuss your next step See AI foundations in practice Explore tailored training

Make good practice repeatable

AI use without shared rules

Bring scattered tools and habits into a consistent way of working.

When useful practices stay with individual developers, results are difficult to repeat. We help teams agree shared setup, ownership, task preparation, and verification without losing the context of their own code.

What we put in place

Strategy: Agree which delivery problems standardization should improve and establish a baseline.
Process: Define reusable task preparation, handoffs, review responsibilities, and practical onboarding.
Platform: Standardize agent configuration, instructions, tools, permissions, and checks across the teams.

Standardization case

151% higher delivery velocity

Three teams with 15 developers adopted shared agent configurations, training, and verification practices. Delivery velocity increased by 151%.

Shared setup, practical training, and repeatable verification helped all three teams deliver more.

Discuss your next step See how a team standardized AI Explore our approach

Improve from evidence

Agents already in use

Improve execution capacity, task success, and cost with measured changes.

Start with the actual bottleneck. That may be laptop-bound execution, repeated task failures, expensive context, or a review queue that cannot keep up. We establish useful measures and improve the parts of the system that constrain the result.

What improvement can include

Strategy: Choose the outcome to improve and compare it with a stable baseline and workload.
Process: Prepare independent tasks, improve review and escalation, and use failures to guide controlled changes.
Platform: Add the execution, evaluation, routing, context, and cost controls needed for the chosen outcome.

Evaluation and benchmarking case

87% agent success

Two teams reached 87% verified agent success through representative tasks, repeatable evaluation, and targeted improvements.

Repeatable evaluation helped the teams turn failures into targeted improvements.

Discuss your next step See how a team improved agent performance

Explore parallel cloud execution Explore lower token costs

These are separate engagements with different outcomes, not a combined result.

Choose the support that fits.

Tailored training helps your team build practical skills. Ongoing collaboration connects assessment, implementation, adoption, and improvement. We can help you decide which format fits the work ahead.

Discuss your next step Explore tailored training Explore ongoing collaboration

Start a conversation

What would you like your team to achieve with AI?

Tell us how your software team uses AI today and what you’d like to improve. We’ll discuss whether tailored training or an ongoing collaboration fits.

Discuss your next step