Will AI replace senior engineers?

No serious roadmap should assume that. We target toil reduction—search, summarization, boilerplate, and runbook steps—while keeping architecture and merge approval human-led.

How do you prevent secrets and PII leaking into prompts?

Scanner hooks, allow-listed repositories for indexing, redaction in RAG pipelines, and client-side policies for IDE assistants. We align with your existing secret management and data classification.

What is a strong first pilot?

An internal documentation and API Q and A assistant with citations, measured on correct endpoint answers and reduction in duplicate tickets to platform teams.

Do you fine-tune our code base?

Often retrieval and tool use get most value first. Fine-tuning is considered only when evaluation, privacy, and cost cases are clear.

ENGINEERING

AI for software development with merge-ready discipline

IDE copilots landed fast; production wins need retrieval over your real docs, evals when answers drift, and workflows that respect protected branches. We build internal developer assistants, incident copilots, and agent patterns that platform and security teams can approve—not shadow IT chat windows trained on the public web.

Developers find APIs, runbooks, and onboarding paths with citations from Confluence, Git, and OpenAPI—you control what is indexed
On-call gets suggested steps from past incidents and playbooks, not hallucinated command lines without review
Leaders see usage and quality metrics so pilots expand on evidence, not hype

Book a fit call Request an assessment

We respect code ownership, license boundaries, and air-gapped options when required.

Cited

Answers tied to your repositories and wikis

Tested

Regression sets before org-wide rollout

Scoped

RBAC for repos, environments, and customers

Owned

Humans remain accountable for merges and production

REALITY CHECK

What breaks without guardrails

Failure modes

Generic models suggest deprecated APIs. Incident bots propose commands that worked once in a fringe stack. Sensitive stack traces land in SaaS logs because no one classified data flows.

Our approach

Grounding, tool contracts, and human checkpoints for risky operations. We integrate with your IDP, logging, and change management—not parallel AI lanes IT cannot see.

USE CASES

Where engineering orgs win

1) Internal developer portals and API Q and A

New hires and partner teams stop pinging your seniors for base URLs and auth quirks when retrieval is current and versioned.

2) Incident and postmortem intelligence

Summaries and suggested mitigations from similar tickets—always with links—not a replacement for the commander.

3) Code review and change-risk assist

Highlight possible regressions and missing tests; reviewers stay in control of approve or request changes.

4) Support engineering and L3 deflection

Customer issues mapped to known bugs and docs before escalating—measured deflection quality, not just volume.

5) Agent workflows with tool use

Ticket labeling, changelog drafting, and environment checks via approved APIs—scoped credentials and audit logs baked in.

AI agents overview RAG for engineering docs

GOVERNANCE

Security by design

SSO, VPC or hybrid deployment options, secrets scanning, and model routing policies. We document what is indexed, how embeddings age out, and who can access which tenancy.

FAQ

Quick answers

Replace senior staff?

No—we reduce search and repetition so seniors focus on design.

Secrets in prompts?

Block, redact, and train alternatives aligned to your policies.

First pilot?

Doc RAG with citations for internal API consumers.

Fine-tuning?

Only when evals justify it after strong retrieval baselines.

Ship a measurable engineering AI pilot

Tell us your stack, compliance needs, and top three friction points. We will propose scope and metrics your leads can defend.

Book a call Email details