AI transformation that survives contact with your data, your compliance team, and your P&L.
We build AI into how the business actually operates: a privacy-first multi-LLM architecture, workflows chosen on evidence, human oversight designed in, and impact measured against baselines. Never boxed into OpenAI, Anthropic, or anyone else.
Architecture before use cases: a privacy gateway that classifies every request, private open-weight models (Llama, Mistral, Qwen, DeepSeek) inside your own boundary for sensitive data, public frontier APIs (Claude, GPT, Gemini) for redacted tasks, and a router choosing per task on cost, quality, and privacy. Then pilot two or three baselined workflows and expand on measured results — not vendor enthusiasm.
Most AI programs fail the same three ways: they deploy on data that can't support the ambition, they wire a single vendor's SDK into production and inherit its lock-in and its bills, or they automate a broken workflow and get faster breakage. Corelynx sequences it correctly — data and process readiness first, the gateway-and-router architecture second, then workflow pilots with before/after baselines and human review logic. The result is AI as an operating capability: private where it must be, frontier-powered where it helps, measurable everywhere, and swappable as the model market reshuffles every quarter.
- Executives under board pressure to 'do AI' who refuse to do it recklessly
- Companies in regulated or trust-sensitive industries — finance, health, services
- Operations leaders with obvious manual-workflow pain and unclear ROI paths
- Teams already burned by a pilot that demoed well and deployed never
- Competitors are shipping AI-assisted operations while yours are debated
- High-volume manual workflows are the visible tax on every growth plan
- Compliance or clients ask where your data goes and the answer is unclear
- An AI pilot impressed everyone and then quietly died in production
- You're about to sign a single-vendor AI contract that smells like lock-in
What this problem looks like from the inside.
The structural causes underneath the symptoms.
Use cases were chosen by excitement
The impressive demo beat the valuable workflow. Value, data readiness, risk, and change burden — scored honestly — pick different winners than enthusiasm does.
The boundary was never designed
Without classification and redaction at a gateway, every integration is a privacy decision made implicitly, discovered eventually, and explained painfully.
One vendor became the architecture
SDK wired straight into production code means the model market's quarterly reshuffles are your re-platforming projects — and your inference bill has no competitor.
Nothing was baselined
Cycle time, error rates, and cost per unit were never captured before deployment — so 'is it working?' has no honest answer, and the program can't defend its budget.
Public and private LLMs, orchestrated. Never boxed into one vendor.
Sensitive data is classified at the gateway and served by open-weight models running inside your boundary — your VPC or on-prem, nothing leaves. Redacted, non-sensitive tasks route to whichever frontier API wins on quality and cost for that task. The router decides per task; you're never locked to OpenAI, Anthropic, or anyone else.
Model-agnostic by design
Every workflow is built behind an abstraction layer, so models can be swapped as the market moves — and it moves quarterly. Vendor lock-in is an architecture failure, not a procurement inevitability.
Privacy boundary first
Classification and redaction happen before any model sees a token. Sensitive content is served by open-weight models inside your infrastructure; the audit trail proves it, call by call.
Right model, right task
Frontier APIs for hard reasoning; small local models for high-volume classification and extraction. In our implementations, routing on cost, quality, and privacy per task has typically cut inference spend 40–70% versus single-vendor defaults.
How Corelynx runs this work, phase by phase.
Readiness before ambition
Score data quality, process determinism, privacy posture, and org capacity per candidate workflow — the map of what to pilot now, prepare next, and defer honestly.
- AI readiness audit
- Opportunity scoring
Stand up the boundary
Privacy gateway — classify, redact, audit — plus the model router. Sensitive data to open-weight models in your VPC; redacted tasks to the best frontier API per task.
- Privacy gateway
- Multi-LLM router
Pilot on baselines
Two or three workflows, before-metrics captured, human review thresholds designed in. Thirty and ninety-day measurement against baseline — impact you can show a board.
- Baselined pilots
- Human-in-the-loop
Industrialize what works
Winning pilots harden into governed capabilities: monitoring, prompt and model management, cost governance, and documented failure handling.
- Production hardening
- Cost governance
Expand as an operating capability
A workflow-by-workflow roadmap, quarterly model-market reviews (swaps are config changes, not projects), and the internal skills transfer that makes it yours.
- Capability roadmap
- Skills transfer
Explicit deliverables. No mystery boxes.
What changes when this works.
Sensitive data provably inside your boundary
AI spend routed to the cheapest model that clears the quality bar
Impact measured against baselines, not vibes
Vendor swaps as config changes, not projects
An organization that owns its AI capability
AI Transformation Readiness Assessment
AI Transformation Readiness Assessment
Answer for how things are — not how the last vendor deck described them. The context: MIT’s State of AI in Business 2025 found ~95% of enterprise GenAI pilots deliver no measurable P&L impact despite $30–40B invested; buying or partnering succeeds ~67% of the time while solo internal builds succeed at roughly a third of that; and Gartner projects over 40% of agentic AI projects will be cancelled by end-2027. The 5% that win share exactly the six traits below.
For your #1 candidate workflow: where does the required data live, who owns it, how current and complete is it — answerable right now, without convening a meeting? MIT’s analysis points to data readiness as the leading structural cause of the 95% failure rate. Score 5: documented lineage, queryable today. Score 1: ‘it’s in a few systems’ is the whole answer.
Hand the workflow’s written procedure to a new hire — could they execute it without folklore? An AI pointed at an undocumented, exception-riddled process automates the chaos at machine speed and metered cost. Score 5: documented, exception-mapped, actually followed. Score 1: the process lives in two veterans’ heads.
One sentence each: what data may leave your boundary, what must not, and how that is enforced — sentences your compliance function would sign today. MIT found shadow AI in over 90% of firms; unstated policy is unenforced policy. Score 5: written policy, enforced at a gateway, auditable per call. Score 1: enforcement is ‘we trust the team.’
For each candidate workflow: what does a wrong output cost, who reviews before consequences, how do errors surface? Gartner’s projected 40%+ agentic cancellations trace mostly to skipping exactly this design. Score 5: review thresholds and escalation logic designed per workflow, in writing. Score 1: ‘the model is usually right.’
Cycle time, cost per unit, error rate, throughput for target workflows — measured today, before deployment, so impact becomes arithmetic instead of argument. The measured baseline is the single most consistent trait of MIT’s successful 5%. Score 5: baselines captured, owned, and dated. Score 1: success will be ‘people seem happy.’
Who owns model governance, prompt management, and cost monitoring — as written responsibility with allocated hours? MIT’s buy-and-partner deployments succeed at ~67% precisely because capacity was honest about itself. Score 5: a named owner, real hours, affected teams engaged early. Score 1: ‘IT will handle it’ — and IT hasn’t been told.
Direct answers, on the record.
No. Sensitive content is served by open-weight models — Llama, Mistral, Qwen, DeepSeek — running inside your VPC or on-prem; only classified-and-redacted tasks route to public frontier APIs. The gateway logs every call, so 'where does our data go' becomes a query, not a shrug.
Whichever wins the task this quarter — and it changes quarterly. Every workflow sits behind an abstraction layer with a router selecting on cost, quality, and privacy per task, so swapping Claude, GPT, Gemini, or a local model is configuration, not surgery. Single-vendor lock-in is an architecture failure.
Readiness audits run $7,500–$20,000 fixed. Gateway-and-router foundations plus first pilots typically run $35,000–$120,000. Ongoing capability management — monitoring, tuning, expansion — runs $2,000–$8,000/month. In our routing implementations, task-fit routing has typically cut inference spend 40–70% versus single-vendor defaults.
See where yours lands →First baselined pilots reach production in four to eight weeks; 30-day measurements follow immediately after. The speed limit is usually data access and review-logic design, not model capability.
Yes — common and productive. We audit what exists, wrap it in the gateway and measurement discipline, and keep what earns its place. Sunk pride is not a reason to sunk more cost, in either direction.
Related practices
Talk this through with a practitioner.
Bring your assessment result. The first conversation is about context and fit — nothing more.
Book a Strategy Session →Or request a tailored roadmap.
Tell us the situation; we'll outline how we'd sequence the diagnostic and what it would examine. Or see the engagement model first.
Request a Tailored Roadmap →