Services
Where we help
We place senior consultants across four areas: AI engineering and AI QA, QA automation, DevOps and cloud engineering, and software engineering. Most clients start with one or two engineers and expand once the working relationship is proven.
Also available
- Technical leadership and architecture review
- Fractional QA or AI lead
- Codebase and pipeline assessments
- Team training and pairing
Service details
What each engagement includes, the methods our consultants use, and the tools they work in.
AI Engineering & AI QA
Two roles that work as a pair. AI engineers build LLM-backed features and the retrieval, prompting, and infrastructure behind them. AI QA engineers build the evaluation harness that says whether those features actually work — before a customer finds out they don't.
What clients typically see
- Prompt and model changes reviewed with data instead of vibes
- Regression baselines that catch quality drops before release
- Fewer hallucination and prompt-injection incidents in production
- Predictable inference cost per feature
What we deliver
- Working feature integrated into your product, not a notebook demo
- Evaluation suite with baselines, run automatically on every change
- Documented model choice with cost and latency tradeoffs
- Guardrails, fallbacks, and monitoring for production traffic
Methodologies
- Retrieval-Augmented Generation (RAG)
- Prompt engineering & versioning
- Offline and online evaluation
- Adversarial and red-team testing
- Human-in-the-loop review design
Technologies
- OpenAI / Anthropic APIs
- LangChain / LlamaIndex
- Pinecone / Weaviate / pgvector
- Python / TypeScript
- Ragas / promptfoo / custom evals
- MLflow / Weights & Biases
QA Automation
Automation frameworks and testing strategy that catch regressions earlier and give your team confidence to release on a normal schedule instead of a heroic one.
What clients typically see
- Shorter regression cycles
- Broader, more reliable coverage
- Fewer bugs reaching production
- Testing built into the pipeline, not bolted on
What we deliver
- Automation framework your team can extend without us
- Test suites wired into CI with meaningful failure output
- Documented test strategy covering what to automate and what not to
- Handover and pairing so the suite survives after the engagement
Methodologies
- Test-Driven Development (TDD)
- Behavior-Driven Development (BDD)
- Risk-based testing
- Continuous testing
- Exploratory testing charters
Technologies
- Playwright / Cypress
- Selenium WebDriver
- TestNG / JUnit / pytest
- REST Assured / Postman
- Appium
- k6 / JMeter
DevOps & Cloud Engineering
Infrastructure and deployment pipelines built with code, monitored properly, and documented well enough that the next engineer can pick them up.
What clients typically see
- Faster, more predictable deployments
- Improved uptime and real observability
- Lower infrastructure spend
- Fewer manual steps between commit and production
What we deliver
- Reproducible infrastructure defined in code and version controlled
- Deployment pipeline with rollback and environment parity
- Dashboards and alerts tied to user-facing symptoms
- Runbooks for the failures that actually happen
Methodologies
- Infrastructure as Code
- GitOps
- Site Reliability Engineering
- 12-Factor App principles
- Blue/green and canary deploys
Technologies
- AWS / Azure / GCP
- Kubernetes / Docker
- Terraform / CloudFormation
- GitHub Actions / Jenkins / GitLab CI
- Prometheus / Grafana / Datadog
- ELK / OpenTelemetry
Software Engineering
Full-stack engineers who ship features, modernize legacy systems, and raise the standard of the codebase they work in without rewriting everything on the way out.
What clients typically see
- Faster feature delivery
- Fewer regressions from legacy code
- Cleaner, better-tested codebase
- Architecture that holds up as usage grows
What we deliver
- Features shipped to production on your existing process
- Architecture decisions written down and justified
- Incremental modernization plan with reversible steps
- Code review and mentoring for your in-house engineers
Methodologies
- Agile / Scrum / Kanban
- Domain-Driven Design
- Microservices where warranted
- Event-driven architecture
- Trunk-based development
Technologies
- React / Next.js / Angular / Vue
- Node.js / Python / Java / .NET
- GraphQL / REST APIs
- PostgreSQL / MongoDB
- Redis / Kafka
- TypeScript
How we scope AI work
A typical AI engagement runs six to ten weeks from framing to handover. The order matters: we build the way to measure quality before we start chasing it.
Timelines shift with data availability and how much integration work sits around the model. We'll give you a revised estimate after the first week rather than defending the original one.
Feasibility & framing
Week 1
We define the task precisely, decide whether a model is the right tool, and set the quality bar the feature has to clear to be worth shipping.
Output: Written problem definition and success criteria
Evaluation harness first
Weeks 1–2
Before tuning prompts, we build the golden dataset and scoring method. Without it, every later decision is guesswork dressed up as progress.
Output: Golden dataset, scoring rubric, baseline scores
Build & iterate
Weeks 2–6
Retrieval, prompting, and application code developed against the harness, with every change measured against the baseline before it merges.
Output: Working feature with measured quality deltas
Harden for production
Weeks 5–8
Guardrails, injection testing, rate and cost controls, fallback behavior, and monitoring on live traffic quality — not just uptime.
Output: Guardrails, alerting, and an on-call runbook
Handover
Final weeks
Your engineers learn to run and extend the evals. The point is that quality stays measurable after we leave.
Output: Documentation, pairing sessions, CI-integrated evals
How we get started
A straightforward process to get the right consultants onto your project without a month of procurement theater.
Discovery call
A working conversation about your stack, the problem, and the constraints you're operating under — technical and otherwise.
Team assembly
We match consultants to your specific needs and send real profiles. You interview them and decide, same as any hire.
Onboarding
Consultants get access, meet the team, and start on something small and real in the first week rather than reading docs for a month.
Ongoing delivery
Weekly written updates, a regular check-in on scope and direction, and a quarterly review of whether the engagement still makes sense.
Most engagements begin within one to two weeks of initial contact. If we're not the right fit, we'll say so on the first call.
Ready to talk about your team's needs?
We'll walk through your project and recommend the right mix of consultants. No obligation to move forward after the first call.