Most consultancies teach AI transformation without ever shipping a product. We do the opposite. AgileAI is the exact methodology we use to ship two SaaS products with a small team — documented, structured, and delivered to yours, in English or Spanish.
Not "prompt engineering workshops." A complete software delivery lifecycle where AI assistance is built into every phase — with the guardrails to keep quality high and the patterns to make it repeatable.
Domain interviews transcribed and distilled into structured specs by a Claude-based pipeline. Human reviewers validate against prior patterns before a line of code is written. Typical output: 60-page PRD in 3 days instead of 3 weeks.
Three working variants of a feature shipped to real users inside a week. No meetings, no RFCs, no architecture debates — just live artifacts to react to. We kill two, iterate on one.
Cursor and Claude Code pair with human engineers on every ticket. Tests, API docs, and migration scripts generated alongside code — not as afterthought. Human engineer is the architect, not the typist.
Every pull request goes through an AI review gate calibrated to your codebase, then through the same agentic QA pipeline we run on AgileWeight's monthly release — autonomous test generation, execution, and triage. Ship daily, not quarterly. The product evolves with the customer — no quarterly release ceremonies, no regressions leaking to customer sites.
We build domain-specific agents your team keeps — a customer-ticket summarizer, a schema-migration generator, a compliance-report drafter. Each one replaces hours of human toil per week.
Every engagement produces something you keep — a system, a playbook, a working agent, an eval suite, an observability dashboard. We don't sell slide decks.
We audit your current software delivery lifecycle and rebuild it around AI-first practices — from planning and PRDs to code review, testing, and release. Delivered as a written methodology your team keeps, not a consulting deck.
Hands-on pairing with your engineers on real work from your roadmap. We ship production features alongside your team while building AI-first muscle memory. Ends with a playbook and a 4-week post-support window.
Small, tightly-scoped agents for the workflows that repeat in your business — specification analysis, ticket triage, code review helpers, documentation generation. For harder problems — like the agentic protocol-detection system we shipped in AgileWeight — we build the reasoning loop, the tool integrations, and the confidence-scoring boundary. You own the agent, the prompts, and the evals.
Production-grade in-product assistants — like the one shipping in the current AgileWeight release. Not "we call OpenAI." The assistant drives, the deterministic core decides: confirm tools never auto-execute — only a user click triggers a write — and the context whitelist is enforced in the core, not in the prompt, so sensitive and tax data never cross the LLM boundary. Every turn lands in an audit log, executed or not. Answers grounded on your docs and cited. Three deployment tiers (offline / BYOK / managed gateway), plus curated content packs for channel partners. We deliver the assistant, the config UI, the retrieval architecture, and the runbook.
The flagship AgileAI capability. Fully agentic QA: autonomous test generation via multi-step reasoning about your codebase, autonomous test execution across real integration paths, autonomous triage that separates a real regression from a flake in seconds. Agents that work — plan, act on the CI, observe the outcome, decide the next step. Not scripts with an LLM wrapper. We build the agents, the toolchains they call, the safety fences, the observability, and the runbook — tuned to your codebase, your CI, your risk tolerance. Ships as a maintained agentic system, not a proof of concept.
Prototype-shop AI consultants deliver a demo and move on. A production AI system needs measurement, retrieval discipline, safety architecture, cost management, and an honest position on fine-tuning. We take a position on each.
How do you know your agent actually works? We design the golden dataset, build the regression suite that runs on every change, and set up the drift detection that catches quality decay in production. Evals are a first-class deliverable — the way you prove the system holds up, and the way you know when it doesn't.
Retrieval quality is the difference between a useful assistant and a confusing one. We design the chunking strategy, the retrieval evaluation, the citation architecture, and the grounding-boundary enforcement — the discipline that lets you ship an assistant that operators trust with real work, not just a demo that impresses in a slide.
What data crosses the LLM boundary? What actions is the agent allowed to execute? How does it behave against prompt injection, jailbreak attempts, and adversarial inputs? We design the safety architecture and the appropriate-refusal behavior, then test it — not once at go-live, but as a standing discipline. Regulated industries expect this; unregulated ones will soon.
Token economics, latency SLOs, error budgets, quality drift over time. Shops that only ship prototypes never learn this; shops that maintain production AI systems live it every day. We install the dashboards, the alerts, the cost-attribution model, and the runbooks for the day something goes wrong. Everything you'd want if you were on-call for the system yourself.
Our position: most clients shouldn't fine-tune. The frontier models are strong enough, the tooling is stable enough, and RAG plus prompt engineering solves 90% of what people think fine-tuning would. When you should fine-tune: consistent output format at scale, domain vocabulary the base model doesn't know, latency-critical paths where a smaller distilled model beats a larger frontier one. We tell you which camp you're in — honestly, not defensively.
Evals feed observability. Observability drives safety guardrails. RAG architecture is what your eval suite measures. Fine-tuning decisions depend on cost data. Serious shops sell these as one system, not five bolt-ons. We do too.
All ten lines scoped per engagement. We don't publish fixed SKUs for work that's still customized around your codebase, your risk model, and your team.
Across a recent release cycle of AgileWeight — one calendar month — we shipped eight releases touching the operator surface, the administrator surface, security, and integrations. Zero lost weighments. A four-front security hardening program. All in production. Zero breaking changes.
Specific AI capabilities that shipped. Each of these is live in AgileWeight today, in customers' hands, in Spanish and English. Each is also a service line we can install in your team.
A conversational assistant embedded in AgileWeight that helps operators without ever taking unilateral action on the system. Retrieval-grounded against product docs, three-tier deployment (offline default, BYOK cloud upgrade, curated content packs). The pattern is deliberate: the assistant guides, the wizard decides. This is the shape we'd build for you when the customer surface can't tolerate autonomous writes.
The auto-detection wizard for industrial scale indicators. Library match first (known protocols), then LLM-based agentic reasoning for unknowns, then a manual decoder as the last-resort fallback. Turns a 4-hour bench setup into a guided flow. The pattern generalizes to any legacy-integration problem where inputs are messy but structured.
LLM-driven adaptation of field labels and terminology to the customer's industry — mining vs waste vs agriculture vs food — with no code deploy or config swap. The setup wizard detects the vertical and adapts the UI in place. The pattern: vocabulary as a first-class runtime input, not a build-time constant.
Autonomous test generation, execution, and triage against every release. Runs in our own CI and gates every AgileWeight deploy — that's how we ship a monthly cadence with zero breaking changes. When you hire us to build QA agents, you get the exact system we ourselves depend on to sleep at night.
The core principle: the assistant drives, the deterministic core decides. The context layer is built before the model is touched, no consequential action auto-executes, operation and cost are visible from day one, and no external capability claim goes out without an audit. Failed gates trigger improvement loops, never silent progress.
Target users, use cases prioritized by value and risk, every candidate action classified as read or confirm, a draft data whitelist, and the deployment tier decision. It ends in a formal committee with your leadership, not in an email.
Exit gate: a go/no-go decision on record. On a no-go the project closes cleanly without moving into build.
The context layer is built before the model is touched: a domain corpus versioned by category and mapped to your product version, plus a golden-question set validated by your domain experts. It targets the cause of 70–85% of assistant failures — the data layer, not the model.
Exit gate: retrieval coverage ≥ 90%, measured and documented. Below the threshold the corpus is rewritten and re-evaluated; we do not move on.
Server-side orchestrator with keys out of the browser and the model pinned by configuration, provider abstraction with swappable adapters, the Confirm-Tool pattern, and the context whitelist enforced in the deterministic core — not just in the prompt. Per-turn audit log: every proposal is traced, executed or not.
Exit gate: adversarial checklist passed — no confirm tool auto-executes and every write traces back to a human confirmation.
Graceful degradation cascade — provider down → fallbacks → deterministic offline mode — with an honest mode chip in the UI, never silently. Per-turn telemetry with no PII or tax data, a p50/p95/p99 latency and cost-per-resolved-question dashboard, SLO alerts and a monthly cost ceiling.
Exit gate: a provider outage degrades with a visible notice, telemetry flows to the dashboard, and alerts are proven with a deliberately triggered violation.
We attack the boundary before a third party does: force a confirmation, extract a key, fire a confirm tool without a click, cut the network. Plus a targeted code audit of whitelist and Confirm-Tool enforcement in the core. The adversarial checklist becomes a permanent release gate, not a one-off check.
Exit gate: an audit opinion with no open critical findings. One critical blocks the pilot and blocks any external capability claim.
Real pilot users, a short briefing on what the assistant proposes and what the human confirms, and a provider-parameterized evaluation run against the weakest configured model. We measure latency, cost, fallback rate and coverage against the SLOs agreed in F1.
Exit gate: a complete go-live checklist with evidence per item and a Technical Director sign-off record. No sign-off, no go-live.
The recurring maintenance almost nobody in the market answers for: scheduled and per-release re-runs of the golden questions, monitoring of the corpus ↔ product version mapping, periodic provider-adherence probes against the live model, and a monthly FinOps review.
Exit gate: a continuous monthly cycle with the report delivered. The internal gate is sustained ≥ 90% coverage; if drift invalidates the corpus, a scoped F2 cycle opens.
In none of the three tiers does data outside the approved whitelist cross the boundary, and in none does a confirm tool auto-execute. What changes is where the model runs, who manages the keys, and what you need in order to operate it.
No external model: the deterministic core resolves, with an optional local model. No data crosses the LLM boundary. Requires local hardware if a local model is used. This is the tier for air-gapped sites or where regulation forbids data egress.
The model runs on the cloud provider you contract, under your own keys. Only the approved whitelist crosses. Built for clients with their own cloud contract and an IT team that administers it.
The model runs through the gateway AgileAI manages: you never handle keys and only need outbound connectivity. Only the approved whitelist crosses. The turnkey tier for clients without a cloud team.
Selection criterion: regulatory constraint first — if the data cannot leave, the tier is Offline; then your team's operational capacity. The tier can evolve: a pilot on Gateway can migrate to BYOK in production via change request.
Most US AI consultancies cannot deliver an engagement in Spanish. Most LATAM agencies cannot show a reference case study to a US-based decision-maker. We are both, with the same playbook, the same team, and the same shipped products behind us.
A 12-week engagement runs entirely in English in Chicago, or entirely in Spanish in Mexico City, with the same senior team and the same deliverables. Not a translation layer — native fluency.
AgileWeight is in production with a customer in La Paz. AgileService enters design-partner beta in Q3 2026. Every practice we teach was forged on a real product a real customer uses.
Every tier has a published price. Every tier is fixed-fee or monthly retainer. No hourly billing, no sales-cycle stretching, no change orders for scope we already agreed to.
Equivalent scope to a Deloitte or Accenture transformation project at a fraction of the price. Because we are specialists, not generalists with a global delivery tax baked in.
Pick the depth that matches where your team is. Every engagement ends with a written playbook your team keeps — and every price is public.
A two-week deep dive. We audit your current SDLC, map the 3–5 highest-leverage AI insertion points, and deliver a 90-day transformation roadmap with phased deliverables.
The core product. Hands-on pairing with your team through a real feature on your roadmap. We ship production work while building AI-first delivery into your team's muscle memory.
An Agile principal embeds as your fractional CTO. Strategic and hands-on — they ship code, hire engineers, institutionalize the methodology, and represent technology in board meetings.
The three tiers above are project-shaped. A production assistant does not degrade on go-live day: it degrades when your product changes and the corpus falls behind, or when the provider shifts the model's behavior without notice. Assistant Care is the retainer that watches exactly that.
If drift or a product change invalidates the corpus, the retainer triggers a scoped F2 cycle instead of letting coverage decay silently.
Talk about Assistant Care →Pricing matches 2026 published benchmarks from fractional CTO marketplaces (fractionalctoexperts, Kompella, Fractionus, CTOx). LATAM pricing reflects the same 40–60% regional differential applied elsewhere on this page. All packages include a 2-week trial window and month-to-month terms after the first 3 months.
Every practice in the AI-SDLC was forged on real products — AgileWeight and AgileService. If something didn't survive contact with a real customer, it didn't make it into the playbook.
Full weighbridge platform with Spanish/English UI, SIAT tax integration, and local-deployment install flow. Built by a 3-person engineering team using AI-SDLC practices. First customer live in La Paz, Bolivia — onboarded and trained entirely in Spanish.
See AgileWeight →Field service management for US minority contractors. 40+ contractor interviews distilled to structured specs by AI, validated by human reviewers, shipped as three parallel prototypes. Beta launching with 20 US design partners.
See AgileService →Book a 45-minute working session. We'll look at a real feature on your roadmap and show you concretely how AI-SDLC would change the delivery — before you sign anything.