AI-Accelerated Delivery
Flex Solutions runs AI-accelerated delivery as an engineering practice, not a slide deck. The ones where growth is real but a system like billing, multi-tenancy, or onboarding is starting to break under real production load, the kind of thing that shows up as a pricing dispute at full sync or a customer ticket you cannot explain, we discover this while working. We build the AI features you actually need, wire the coding agents into your repo, and put your own engineers back in control before we leave.
Can You Start Without Committing to a Full Engagement?
Yes, and this is the part most AI vendors will not offer upfront: a 1 to 2 week use-case sprint lets you see a scored use case, a fee letter, and an eval seed set before you commit to a 4 to 10 week build.
Two engineers review three or four candidate use cases with your team, score each on impact, evaluability, and delivery risk, and hand back one use case worth shipping, a documented reason for setting the other two aside, and a scoped fee letter. Whether or not you continue past that point, you keep the scorecard and the risk register.
How We Work: From 2-Week Sprint to Full Build
- 01
Scope
Flex Solutions reviews your candidate use cases, scores them on impact, evaluability, and delivery risk, and hands back one use case to ship along with a fee letter, typically within four working days of first contact.
Use-case scorecardRisk registerFee letterWEEK 1 - 02
Eval harness
The eval suite is built before the feature itself, using real transcripts, ground truth data, and pass or fail criteria signed off by you. This runs in CI from day one.
Eval harness in CIGround-truth setPass criteriaWEEK 2 - 03
Build and iterate
Evals run daily. Every prompt change ships as its own pull request, with cost and latency tracked next to accuracy from day one.
Working featureDaily eval runsCost dashboardWEEK 3-8, if you continue - 04
Ship and train
The feature rolls out behind a flag in ramped stages. Your engineers run the eval harness and edit prompts without Flex Solutions by the end of the engagement, with a runbook and 30 days of support left behind.
Ramped rolloutEngineer trainingRunbook30-day supportWEEK 9-10
Deliverables From Our AI Delivery Pod
Most AI consulting is a slide deck and a Notion template. We treat AI as an engineering primitive: agents reviewing PRs, evals replacing flaky manual QA, structured prompts producing schemas, and humans in the loop where the loop earns it. The output is shipped product, not a strategy memo.
A written use-case scorecard ranking the options you brought us, with the one we recommend shipping first.
A working AI-flow practice built inside your team: agents, prompts, evals, and runbooks.
One concrete delivery, such as a feature, migration, or audit, shipped using that practice.
Claude Code and Cursor workflows tuned to your repo, your conventions, and your reviewers.
An eval harness for every prompt that reaches production, seeded from real transcripts and ground truth.
Cost guardrails: spend caps, model routing, fallback chains, and telemetry.
Documentation your own engineers can operate after Flex Solutions leaves.
Capabilities - Inside the Engagement
- 01
Coding agents in your repo
Claude Code, Cursor, and Codex configured with the right tools, context, and human reviewers on every pull request. A configured workflow, not an unsupervised agent guessing at your conventions.
- 02
Eval-first features
Every LLM-backed feature ships with an eval suite seeded from real transcripts. A regression is treated as a failing test, not a judgment call.
- 03
Structured outputs
JSON Schema, Zod, function calling, and constrained generation, so your application receives data it can act on, not prose it has to parse.
- 04
RAG that earns its keep
Embeddings only on data that warrants them, retrieval evaluated against ground truth. Most projects do not need a vector database, and we will tell you when yours does not.
- 05
Cost and latency engineering
Model routing, caching, streaming, and parallel calls tuned to an agreed cost-per-request and latency budget before launch, not after the first bill.
- 06
Guardrails and evals
Prompt injection tests, jailbreak suites, output filters, and redaction, built in from the start so the feature survives past launch week.
Looking for full-stack builds or platform migrations alongside AI? Explore our SaaS Product Engineering and Internal Tools Development services.
The AI-Delivery Stack We Use
These are opinions formed by running evals in production, not vendor preference. We recommend what fits your data and your team, and change our mind when the evidence tells us to.
- + MODELSClaude (Anthropic)GPT-4 / 5GeminiLlama
- + AGENTSClaude CodeCursorCodexLangGraph
- + EVALSBraintrustPromptfooInspectCustom harnesses
- + INFRAVercel AI SDKOpenRouterPineconePostgres + pgvector
We ship production features on a small, evidence-backed stack by default. The list above is our starting point when a client has no strong preference, not a requirement.
AI-Accelerated Delivery vs. the Alternatives
| Comparison factor | Flex Solutions | Hiring a Senior AI Engineer | Traditional AI Consulting |
|---|---|---|---|
| Time to start | 1 to 2 week use-case sprint, embedded with your team | Weeks to months of hiring and ramp-up | Weeks of scoping before code gets written |
| Cost structure | Fixed quarterly retainer or single fixed fee | Salary, equity, benefits, indefinitely | Day rate, scope creep is common |
| Risk if the fit is wrong | Engagement ends at a scoped point, low sunk cost | A bad hire costs months to unwind | Contract disputes over what done means |
| Best fit | A specific, bounded, high-risk AI system that needs to ship and be evaluable | Ongoing AI product ownership, permanent capacity | A genuinely open-ended research question, not a build |
We default to the embedded model because it is the shape that surfaces the real risk before anyone commits to it fully. If what you actually need is a permanent hire or a fixed-scope research project, we will say so before you sign anything.
Numbers From AI Features We’ve Shipped
No. Cursor and Claude Code are part of the toolchain, not the offering. The offering is the eval harness, the cost guardrails, the structured output layer, and the handover runbook built around the agent, so the practice keeps working after Flex Solutions leaves.
Every engagement tracks delivery throughput against a stated baseline from week one, alongside median ship time and eval pass rates, so the speed claim is measured on your own project rather than asserted in general.
Every line still goes through the same code review and test suite your team already uses. Coding agents are wired in as contributors on pull requests, not as a bypass around review.
Yes. Model routing, private endpoints, and self-hosted or VPC-based options are configured based on your data governance requirements, and this is scoped during the use-case sprint in Week 1.
Cost is tracked from day one via a live cost dashboard, with model routing, caching, and fallback chains built in to hit an agreed cost-per-request target before launch, not after.
Yes. Flex Solutions will send its own NDA or sign yours, and treats anything shared in a project brief as confidential by default.
Yes. It is designed to embed inside an existing SaaS build, internal tools project, or CMS migration rather than compete with it for calendar time, since the same two-engineer, one-PM team structure applies.
Get an AI Product Engineering Scorecard, Before You Commit
Book a two-week use-case sprint. You will leave with a ranked scorecard, an eval seed set, and a fee letter, whether or not you hire us for the full build.