AI strategy consulting is worth paying for when it ends in decisions your engineers can build: which use cases to fund, what architecture and controls each one needs, and how it reaches production. At Allerin, senior engineers make that diagnosis, and the same team is accountable for the build. For a company-wide transformation program, a large firm is the better fit.
Nobody searches for a strategy deck. Engineering leaders arrive with one of five concrete problems, and each already has a fixed-scope answer at Allerin, run by the people who would build the fix. Find yours and skip the sprint.
A two-week lab. Senior engineers pick the one use case worth testing, build a working prototype against your data, and end with a build, park or pivot call you can defend upstairs. You also get a plain split of which data is ready now, which can be made ready with effort, and which is not worth it.
Duration: 2 weeks
A fixed-scope audit of your real usage data: where the tokens go, where retrieval fails, which calls should never reach the expensive model. It ends in a fix plan with the leak measured, not estimated. Start with the free three-minute diagnostic; if that already tells you what you need, take its roadmap and run.
Duration: 2 to 3 weeks
One control plane for the models every team calls: routing by cost and latency policy, per-team budgets with hard caps, guardrails that block unsafe actions, and an audit record of what each agent did. The platform readiness check tells you in five minutes where the gaps are today.
Duration: 3 to 5 weeks
Controls mapped to SOC 2, NIST AI RMF, HIPAA and CJIS, with the evidence an auditor or a customer can check: prompt injection, model theft and data poisoning covered, and the records to show it. Startups with an enterprise deal on hold get the SOC 2 fast track.
Duration: 4 to 8 weeks
A production path with gates: the new system runs beside the current one and its outputs are compared, then it takes 5% of traffic, then 25%, then all of it, with monitoring at each step and a rollback that works. The comparison page explains why pilots stall; the MLOps service runs the path.
One artifact per route, each something an engineer can act on the next morning.
The most upvoted question in every buyer thread is “who owns it after launch?” The answer here is one word: you. When an engagement ends you own everything, the code, the models and the infrastructure. It arrives documented and tested, with runbooks, architecture docs and training sessions for your team, and the partner exits cleanly.
That is the same promise the comparison pages make, stated here so it is concrete. Read the partner versus in-house page for what it looks like at the end of a build.
A strategy written by people who will never build it carries no cost of being wrong. A diagnosis made by the engineers who will be accountable for the build does: every recommendation becomes their own scope, their own KPI gate and their own rollback plan. That is why Allerin does not sell a strategy sprint, and why every route above ends in a fixed-scope engagement the same team runs. The long version, with the trade-offs on both sides, is on the boutique versus Big 4 page.
Only things already published on this site, each one checkable.
A free diagnostic that models your actual stack (foundation model, vector store, orchestration) with four deterministic scoring models and hands you a two-page PDF. Competitors offer quizzes.
Run the diagnosticThe audit's own honesty. Its FAQ says "The estimate tells you whether to look. The audit tells you what to fix," and "If the diagnostic already tells you what you need, take the roadmap and run."
Read the audit pageA published measurement method: the windows, formulas and artifacts behind every number we report, agreed before work starts.
How we measureRunnable evidence you can download: a model rollback demonstration and an agent guardrails workflow with expiring approvals. Both synthetic, both yours to run.
Resources100+ AI and ML systems built, almost all still running in production. Several have run for more than six years; the retired ones were business decisions, not technical failures.
Customer storiesA company-wide transformation program across many business units, a multi-year roadmap that needs change management in every region, or a board that wants a global brand on the cover: that is a large firm's work, and our comparison page says so. Allerin's ground is narrower: the engineering decisions that decide whether a given AI system ships and keeps running. If your question is the first kind, take the large firm. If it is the second, the routes above are the fastest way to an answer.
Three minutes. Models your LLM and RAG stack and quantifies the leak.
Five minutes. A score and the first recommendations.
Twelve questions on infrastructure as code, deploys, spend and resilience. No email required.
Payback estimates for agentic AI, computer vision and modernization.
Thirty minutes with a senior architect to name the problem and the fixed-scope answer, or three minutes with the diagnostic to see the leak in your own stack.
