Enterprise AI, delivered end-to-end.

Tangible, senior-led programs (from strategy through to systems we run) for the problems no product covers yet.

The SQE Suite. Deploy in 30 days.

Productized AI you can switch on for a single use case, on a single site, then scale by results.

Documentation rarely explains why. Learners interrogate it through Claude instead. One learner wired the full Grafana Alloy stack on EC2, with exporters for PostgreSQL, Redis and RabbitMQ, and came out understanding how the pieces talk to each other.

Learners paste errors and get root-cause reasoning. One learner worked a Permission denied (publickey) failure step by step and came out understanding the SSH key chain. Another pasted an API token mid-flow, and Claude stopped to tell them to revoke it.

Security checklists, provisioning scripts, runbook drafts and pre-commit hooks come out of one prompt. A peer-built skill called grill-me interrogates the learner on details until the brief is tight enough to act on.

Learners now ask what and why before touching code, including the questions a guided activity does not cover. They draft career roadmaps, outlines and plans with Claude first, so they lead the mentor discussion instead of walking in cold.

A Claude-powered bot reviews every push and comments on the pull request. Findings are advisory and do not block merge. A second team went further: when a test fails, the agent fixes it, commits and pushes back to the same PR. Four scripts and one deployed agent.

When an application goes down, a bot fetches logs and metrics from Grafana, reasons about the cause, and posts a summary and a suggested fix to Discord or Telegram. It resolves the alert when the service recovers.

Exposes the Loki, Grafana, Tempo and Mimir stack to an agent through 16 narrow, schema-checked tools: read-only senses for CPU, memory, error rate, p95 latency, alerts, logs and deploys, plus vision through dashboard screenshots. No broad shell or SSH access, on purpose.

Five MCP servers, for Grafana, AWS, SonarQube, Jenkins and Git, behind one tool registry. Claude checks incidents, reviews or fixes pull requests and reports back in chat. AWS access is enforced read-only, and an admin panel toggles tool groups per chat.

On every PR the AI reads the Terraform plan and posts one comment: what changes, the risk level, a fix per issue. Nightly it compares live infrastructure to code, reports drift, and asks which side to trust. Only if you choose live does it edit the Terraform and open a pull request.

Linters catch what a syntax-tree rule can express. Tertib catches the rest: naming styles, file-structure expectations and layering boundaries, written in plain-language YAML. It sends changed code to the model and fails CI when it matters.

Learners take the just-in-time questions to Claude first: the what, the why, the error message. Mentors keep the formative assessments, the how-and-why questioning and the judgment calls. That let cohort 1 hold a mentor-to-learner ratio of 1:10.

Mentors draft guided activities with Claude, using a mentor-built skill called concrete-first-teaching that starts every concept from a concrete case. Learners rate each activity afterwards. Ten or more activities so far average 4.8 out of 5.

Mentors run the cohort’s AWS estate: per-learner IAM policies and permission boundaries, billing alerts, cost audits after a real Grafana cost spike, and self-hosted Bitbucket runners on EC2. Claude Code drafts and explains. A mentor stays the only one who applies.

A question like why fee income fell at the securities arm could not be answered from the pack. It triggered an email to a business unit finance head, a request to an analyst, a new spreadsheet, and a wait of 3 to 5 business days.

The financials tell you what happened, not why. A margin moves or a fee line softens, but the cause sits inside a business unit’s operations, in a different system, measured a different way, often under a different definition of the same word. Bridging the two took a manual reconciliation every time, so the answer came late or never.

Consolidation, variance commentary and slide production were manual and repetitive. The work had to be redone from scratch each cycle because nothing upstream was standardised.

Regulated and customer-identifying data stays in the country, deployable inside SMMA’s own tenant. The layer that coordinates the AI agents, and any non-sensitive workloads, run on managed infrastructure, where elasticity is worth more than control.

The agents that answer questions, assemble dashboards and write the monthly narrative run on the Claude Platform, with the Files API handling the documents executives bring to a conversation. SQE builds and operates Polaris with Claude Code and designed its executive interface with Claude Design.

A question becomes a chart, table or written answer, generated from the data warehouse at the moment of asking rather than pulled from a static report.

Polaris assembles a full dashboard from a single request, and executives refine it through conversation.

Data storytelling pages combine charts, key figures and narrative into one document that explains what changed and why, not just what the numbers are.

Dedicated agents can be set up for Finance, Operations, People or other domains, each carrying that area’s own context and terminology.

SMMA moved the monthly management pack into Polaris, which put the platform on the reporting critical path instead of beside it. Polaris became where the month’s numbers were produced, not an optional extra.

Usage is tracked on three measures: how many executives use it each month, how much they ask, and how often an answer is accepted without a manual recheck.