Diffco Services
Most AI transformations don’t fail on model quality. They fail when a rushed rollout breaks a working process and stalls delivery for months. Discovery maps what’s safe to transform, what needs prep first, and what’s revenue-critical and off-limits.
3-week fixed sprint
You own every deliverable
NDA from day one
Led by senior engineersIf you’re still weighing whether AI-native is worth the disruption, this is the size of the prize.
Not autocomplete shaving minutes off a task. When AI works with real context on your system, sprint-sized work lands in days, and work that waited on a specialist gets unblocked. It’s a different throughput ceiling, and once your team crosses it, the old pace is hard to justify.
Understaffed, over-scoped, hiring frozen? AI-native engineering is how a lean team delivers like a larger one. Not by replacing engineers, but by pulling them off the low-leverage work that eats their week, so they spend it on what only they can do.
Your most valuable context lives in a few people’s heads, and walks out the door when they do. AI-native development turns it into durable, structured knowledge the whole org can build on: an asset the company owns, not a person it can’t afford to lose.
The teams waiting this out aren’t playing it safe.
They’re falling behind competitors who already moved.
Who this is for
If any of these sound like your engineering org, this is for you.
AI produces real work, just not reliably enough to trust at scale. Discovery finds what’s blocking reliable output on your codebase, and what it takes to fix it.
AI helps on small tasks but stalls the moment it touches core logic. Discovery shows what’s transformable, what needs prep, and what to leave alone.
AI writes more code than your seniors can review, so quality slips or the queue grows. Discovery shows where review can be automated and how to rebalance the work.
Before you reorganize or commit budget, you need a plan you can defend. Discovery hands you one, in a shape that holds up to a CFO.
No rebuild from scratch, no two-year initiative. Discovery finds the highest-leverage moves for your context, not a generic playbook.
Adoption is uneven, standards are inconsistent, and no one owns the direction. Discovery maps where AI actually works across your team, and what a consistent approach would take.
Diffco AI Methodology
Code is the cheap part now. Knowing what to build first, which foundation will hold at ten times the users, and what not to build at all is where projects are won.
Senior engineers make those calls. More than 100 AI agents per engineer do the building, around the clock, on our own delivery platform built on Claude Code and OpenAI Codex. A living plan with automatic checks keeps every agent inside the lines.
What you get
No slideware. No “best-practices library” we hold the rights to.
How work actually moves from request to release. Where friction concentrates, which paths are revenue-critical, and which points offer the biggest gains.
Where AI-native development is viable today, where it needs prep, and where legacy debt requires bounded rework. Prioritized by leverage and risk.
What documentation exists, where it lives, what’s stale, and what must be captured, including the context in people’s heads that never made it into a doc.
A sequenced plan for 90 days, six months, and beyond, with each step earning the next. Runnable with your team, with us, or with anyone.
Engineer-months by workstream, likely (P50) and worst-case (P90) timelines, and where the risk sits. The cost of doing it, and of not doing it, in numbers your CFO can work with.
A board recommendation in three paragraphs: “start now,” “fix these first,” or “not ready, here’s what to do instead.” We’ve said “not yet” before, and we will again.
How we build
Every project runs through Production-Ready AI Engineering: seven stages, senior engineers directing 100+ AI agents, a check on every save, and a person signing off at every gate.
Workshops and stakeholder interviews with AI in the room. It cross-verifies inputs across calls, docs, and Slack threads, flagging blind spots, edge cases, and contradictions humans miss.
AI decomposes the work, picks architecture, models, and tools. Surfaces risks, costs, trade-offs, and conflicts between stated requirements, before they become bugs.
You review and approve. Every decision is logged: an auditable trail before any code is written. No black box, no scope drift.
AI agents execute the approved plan inside engineered guardrails. Senior engineers steer and unblock. Sprint cadence with working artifacts, not status decks.
Three independent layers on every release candidate: automated evals, senior code review, and dedicated Human QA, with manual and exploratory testing alongside security, performance, and accessibility checks.
Production deployment, UAT, knowledge transfer, runbooks. Cutover is documented, reversible, and on a schedule you approve. Your team takes the keys, or we operate it for you.
Launch is a milestone, not a finish line. Monitoring, feedback loops, and incremental improvements keep the system compounding, and feed the next discovery cycle.
Every later change re-enters the same loop, and nothing skips a step.
Read the full methodologyWhat we bring
Claude Code
Cursor
Coder
Codex
Anthropic
OpenAI
Gemini
Hugging Face
Mistral
Qwen
Grok
Kimi AI
TypeSafe AI
Open weight models
Knowledge graphs
MCP servers
Custom skill libraries
Persona-driven prompt frameworks
Internal spec-driven-development toolkit
Custom eval harnesses
LangSmith
Helicone
Promptfoo
Tree-sitter analysis
Custom reverse-engineering harness
Semantic code searchWhy we diagnose first
A transformation has to fit how your team actually works, not how the process doc says it works. Discovery maps the real workflow first, then closes the gap.
In our experience, most AI ceilings are context problems, not model problems. Discovery finds which context is missing, buried in people’s heads, or recoverable from what you have.
Some codebases are ready, some need prep, and some are too far out. We’d rather tell you in week three than month nine, and show you what to fix first.
Discovery ends with a decision, not a commitment. If the numbers don’t work or we’re not the right fit, either side walks away, and you keep every deliverable.
Senior experts on every project: AI/ML and software engineers, product and UX designers, and project and product managers. Assigned to you, never reassigned mid-project.
Agile delivery on Production-Ready AI Engineering, from early PoC to production rollout. Clear gates, your sign-off at each one, so you always know what’s happening and why.
You always know:
Every agent action is logged and every decision is traceable. SOC 2-aligned practices, HIPAA- and GDPR-aware delivery, and full IP assignment.
From the first workshop to post-launch optimization, you get:
Knowledge transfer, runbooks, and documented, reversible cutovers if your team takes the keys. Ongoing support, monitoring, and improvements if you’d rather we operate it.
Engagement paths
A fixed three-week sprint, quoted after our initial call. The deliverables are yours outright: run the roadmap with your own team, another partner, or with us later.
If Discovery green-lights it and you continue with us, we bring Production-Ready AI Engineering into your team, working from the roadmap you’ve already agreed.
Explore our
amazing case studies
AI Agents DevelopmentCustom agents and multi-agent systems that do real work inside your CRM, EHR, ERP, and internal tools.
AI & ML DevelopmentML, RAG, computer vision, and copilots, engineered to production standards with Production-Ready AI Engineering.
AI-Driven Product DiscoveryTwo weeks to answer “should we build this, and what would it cost to run,” before you spend six months finding out.
Team AugmentationSenior engineers who join your team, fit your workflow and culture, and start shipping from week one.
Web DevelopmentModern websites and web apps engineered by senior teams directing AI agents.
333 W San Carlos St Suite 600, San Jose, CA, USA, 95110
