Methodology
An honest architecture + flow diagram of how NoCode migrates cloud LLM workloads to optimized local inference. The operational methodology is open. The routing algorithms and model selection logic stay proprietary. That is the deal.
Manual founder-led calibration up front. Codified into automated drift insurance on the back end. The boutique-to-autopilot transition is the bus-factor answer, not a "trust us" assertion.
Total opacity reads as lack of substance. Total transparency gives away the moat. This is the line we draw, explicitly.
What CISOs and compliance teams need to validate - without handing engineers a bypass blueprint. The category is open. The per-workload selection stays proprietary.
All deployed models are open-weight foundation models released by established research labs (university research groups, major AI labs with public model cards, reputable open-source consortiums). No internally-trained black-box models. No unverifiable third-party weights.
Models are deployed at 4-bit to 8-bit quantization for optimized local inference. Quantization technique is standard post-training (not a proprietary format). Weights remain in inspectable industry-standard formats at all times.
Specific model selection per workload is proprietary and evolves quarterly as the open-source ecosystem advances. Each rotation is accompanied by a CVE rationale document: previous family, new family, count of patched CVEs, audit date. The cadence + rationale are public. The exact current selection stays proprietary. Your CISO sees the security delta without seeing the blueprint.
All deployed models carry MIT, Apache 2.0, or explicitly permissive commercial-use licenses. Your legal + compliance teams can verify the license of every model in your deployment on request. No copyleft surprises. No training-data lineage ambiguity in the license chain.
For procurement + CISO review:
NoCode provides a per-deployment model lineage document on request, listing each deployed model's category, quantization, license, training-data disclosure status, and any known vulnerability class advisories. This document is for the customer's security-review team only and is refreshed with each quarterly rotation.
Sample CVE rotation entry (format)
Cadence + reasoning are public on this page. Family names live in the customer's private lineage doc. CISOs get the security delta. Competitors do not get the blueprint.
Procurement teams need to know which model families are even on the table before they pre-approve a vendor. The honest answer separates what NoCode deploys from what NoCode routes to. Both pools are tracked. Responsibility differs.
Open-weight foundation model families (permissively-licensed releases from leading research labs). NoCode tracks CVE advisories on every deployed family and rotates the pool when issues land. Pre-approval at procurement covers the category, not the per-workload selection. The deployed pool is the one we own.
Claude, GPT, and Gemini families. CVE posture for these is the upstream provider's responsibility. NoCode surfaces their advisories to customers and rotates the routing pool if a vendor is materially compromised. We do not patch the vendor's model. We do not pretend to.
Procurement pre-approves the pools. Per-workload routing decisions stay proprietary inside the heuristic mapping engine.
Every request entering a NoCode-migrated pipeline hits this decision node first. Routine work goes one way, frontier-grade work goes the other.
The analyzer scores requests on input-shape dimensions. Abstract categories, not implementation details.
Instruction nesting, task decomposition depth, number of distinct sub-goals embedded in the request.
Total token volume, long-document recall requirements, cross-reference span between input segments.
Chain-of-thought length required, ambiguity tolerance, need for novel synthesis vs pattern matching.
Schema rigidity, format constraints, whether the target output is bounded or open-ended.
The four phases every NoCode engagement runs through. Documented. Reversible. Instrumented end-to-end.
We ingest sanitized API usage logs. Call patterns, token counts, timestamps, endpoint shapes. Your prompt contents and customer data never leave your network.
We do not ask "what archetype is your whole company." We rank your endpoints by highest volume + lowest reasoning complexity and identify the top 10-20 candidates for migration first. Surgical targeting beats codebase-wide rewrites. Your messy codebase is not a blocker - we work with the slice that matters most.
Each workload is classified against the four input-shape dimensions above. Each class gets matched to a migration path: frontier-stays, edge-candidate, or hybrid. You see the mapping, the rationale, and the projected savings per class before anything moves.
Customer sets the benchmark criteria and grading rubric BEFORE shadow testing begins. You provide 5-10 representative prompts, your gold-standard responses, and your tonality / format requirements. The heuristic mapping engine is tuned to meet your operational standards, not industry benchmarks like MMLU or HumanEval. For workloads flagged for migration, the optimized edge path runs in parallel with your existing cloud path and both produce outputs. Outputs are compared at the shape level and against your rubric. Zero customer-facing impact during this phase.
Traffic shifts begin with a single non-critical endpoint as a canary (HR chatbot, internal moderation queue, content tagging - whatever your IT team flags as low-blast-radius). Two weeks of canary traffic with full latency, fallback rate, and cost-delta telemetry. CISO and IT validate uptime in sandbox before any revenue-path endpoint moves.
Once the canary clears, traffic shifts gradually from cloud to edge per workload, with an automatic rollback trigger if confidence drops below threshold. The rollback path stays live indefinitely. If a workload ever needs to go back to the cloud, it reverts without a code deploy.
Anonymized industry case study for a mid-market extraction pipeline. Real numbers from public record.
Workload: Multi-field extraction from vendor product descriptions. Classified as structured extraction (bounded output schema, moderate context, low reasoning depth). Flagged for edge migration in phase 2.
Phase 3 shadow testing ran for 6 weeks before cutover. Post-migration confidence stayed above the 98% threshold for the full 12 months of observation. No rollbacks triggered. Frontier path remained live for the catalog's complex-reasoning tier (brand-voice generation, competitive positioning), which was never migrated.
Note: this is the upper end of the extraction tier. This customer's total AI bill reduction, blended across all their workloads including unchanged frontier paths, landed in the 40-50% range.
NoCode is a high-touch managed engagement, not a multi-tenant cloud product. Onboarding is capped on purpose.
Q2 2026 capacity
We onboard three new enterprise portfolios per quarter to guarantee strict SLAs. Strict cap. No exceptions.
If both Q2 spots fill before your discovery call, the audit is still free and your engagement is queued for Q3 onboarding (July 2026). Wait-list signups receive their audit deliverable inside the standard 10-business-day window regardless.
Six terms this site uses that buyers asked us to define. One paragraph each, one analogy each, one concrete example each. No buzzwords, no hand-waving.
Before any revenue-path workload moves, NoCode shifts traffic on a single non-critical endpoint (HR chatbot, internal moderation queue, content tagging). Two weeks of live telemetry. Latency, fallback rate, cost delta. If anything goes sideways, the blast radius is one endpoint nobody's customer sees.
Analogy: An F1 pit-lane mechanic does not change all four tires on the lead car first. He changes one tire on a back-of-grid car, watches the lap time, then commits.
Concrete example: A retailer's order-status chatbot moved to the edge model on Monday. Two weeks of green dashboards. Then their checkout-error-classifier followed. Their highest-revenue workload moved last.
The customer's gold-standard rubric is recomputed continuously. We send synthetic shadow probes through both the live edge model and a frontier reference model, then score the deltas. Drift surfaces on the dashboard before any production user sees a worse answer.
Analogy: A triage nurse who quietly takes a second blood-pressure reading every visit. The patient never feels it. The nurse catches the trend before the patient feels symptoms.
Concrete example: A legal-tech extraction workload starts producing slightly shorter clause summaries. Live users have not noticed. The drift dashboard fires. Retune happens that week. Quality returns above threshold before the next monthly review.
The customer-defined rubric (gold-standard responses + tonality + format rules) gets converted into measurable scores. Schema parity, output-shape distribution, length variance, refusal rate. The math runs against the rubric, not against generic benchmarks. The SLA contract reads from the math.
Analogy: A tailor measuring you for a suit. Every customer has different shoulders, sleeves, taste in lapel width. The garment is fit to your numbers. The MMLU score of the cloth is irrelevant.
Concrete example: "Output should always be a 5-bullet list, citations in section-number form, no hedging language, max 200 words." Each rule becomes a check in the scoring pipeline. The auto-credit clause fires when the aggregate falls more than X% over 30 rolling days.
Pool 1 is the open-weight foundation models NoCode deploys at the edge. We patch CVEs on this pool. Pool 2 is the frontier APIs we route to for complex reasoning (Claude, GPT, Gemini families). Upstream provider patches CVEs on this pool; we surface their advisories to the customer and rotate the routing pool if a vendor is materially compromised.
Analogy: A general contractor builds the cabinets in your kitchen but also installs your dishwasher. The contractor warrants the cabinets directly. The dishwasher carries a manufacturer warranty the contractor surfaces to you. Two warranties, two responsible parties, both honestly named.
Concrete example: A medium-severity prompt-injection CVE drops on the edge pool in Q3. NoCode patches the deployment within 14 days, audit log shipped to the customer's CISO. Separately, an upstream provider issues an advisory for a frontier model. NoCode flags it the same day, recommends the customer evaluate whether to pause routing to that family.
Most engagements transition from manual founder-led calibration into an automated steady state where the routing rules are codified. From that point forward, NoCode's intervention is exception-handling: drift alerts, model rotations, vendor advisories. The customer's day-to-day infrastructure is hands-off.
Analogy: An air traffic control system. The complicated routing logic was built by humans up front. Most of the time it runs itself. The humans are there for the exceptions and the emergencies.
Concrete example: A pharma research customer signs a 12-month engagement. The first 60 days are heavy, hands-on calibration. Months 3 through 12 the engagement is mostly autopilot, with NoCode firing exception responses against the SLA. The customer's CISO sleeps better. The CFO sees the auto-credit ledger.
Mean time to response, written into the SLA addendum. NoCode commits to 1 hour first-response on sev-1 incidents and 4 business hours on non-critical alerts during the active engagement. A documented runbook ships in the daily-regenerated escrow bundle so the customer's on-call engineer can act without NoCode in the loop.
Analogy: A 24/7 plumber who also leaves you a written guide for shutting off the water main yourself. You probably will not need the guide. The guide is why you sleep through the night anyway.
Concrete example: Routing layer returns malformed output to a customer at 3am Sunday. The on-call engineer follows the runbook to roll back to the previous routing rule set in 90 seconds. NoCode acknowledges the incident bridge by 4am. Resolution by 7am. Post-mortem ships Monday.
The audit covers phase 1 + phase 2 end-to-end. You see the full routing recommendation and the projected savings per workload, in writing, before you decide anything.
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