The LLM harness for enterprise AI. One layer that routes, caches, and governs every model call. Up to 50% lower spend, no lock-in.
One contextlayer for everyfrontier model
Clientell routes each request to the right model, caches repeated context, and enforces your security rules. Most workloads can cut LLM spend up to 50%.
Better output, up to 50% lower spend.
One layer over every model and every system
It cuts your AI spend, sharpens answers, and keeps every call governed, on any frontier model you choose.
Frontier models are powerful and ungoverned
The same three gaps show up in every enterprise running LLMs at scale.
Your agents are hard to debug across long context, branching logic, and a dozen tools, and quality drifts quietly between releases.
Inference is now most of your AI budget, and a lot of it goes to reprocessed context, retries, and a model bigger than the task needs.
Teams call models with no shared audit trail, no PII controls, and no clear answer for data residency.
How the harness works
Every request passes through one layer before it reaches a model, and again on the way back.
Request
An employee or app sends a task.
Route
cost leverThe harness picks the cheapest model that still clears your quality bar.
Cache
cost leverRepeated context (system prompts, tools, knowledge) is served from cache, not reprocessed.
Optimize
Prompts are trimmed, and work that can wait is batched.
Frontier model
The chosen model (Claude, GPT, Gemini, or your own) runs the call.
Govern & return
Every call is logged, PII-screened, and access-checked, then the result returns.
The cost lever sits in routing and caching. Cached reads cost about 10% of base input on Claude, and batch runs are 50% off (Anthropic pricing).
Where the savings come from
The number depends on your workload, so we show the math instead of a promise. Three levers, each documented by the model vendors.
Send tasks that do not need a frontier model down a tier. On Claude, Opus to Haiku is about 80% cheaper on input and output; Opus to Sonnet is about 40% cheaper.
A stable prefix (system prompt, tools, shared knowledge) is cached. On Claude, cached reads cost 0.1x base input.
Work that can wait runs through the Batch API at half price on input and output, and it stacks with caching.
Route a task down a tier and the same call costs a fraction
Anthropic list price per million tokens. A request that does not need the frontier tier runs on a smaller model for a fraction of the cost, which is the routing lever above.
Illustrative math from the three levers above. Your real number is sized in the architecture review.
Realized savings depend on how much of your traffic is routable, cacheable, and batchable. A workload already on a small model, with no repeated context and hard real-time limits, will save less.
Built for every model
and every team
up to 50% less spend
fewer hallucinations
every call logged
any frontier model
Clientell sits between your apps and every frontier model. It routes each call to the right model, caches what repeats, governs what matters, and connects to the systems you already run.
One request, routed, cached, and governed
Every workload takes the same path: routed to the cheapest model that clears your quality bar, served from cache where it repeats, and governed on the way out.
Eleven capabilities, four jobs
Cost, quality, governance, and reach, in one layer.
Cost
Cut LLM spend up to 50%
Model routing, caching, optimization, and orchestration on every call.
Quality
Better, steadier output
Improve consistency and reduce hallucinations with quality gates and evals.
Insights that took analysts days
Surface answers across your data that used to take an experienced analyst days or weeks.
Governance
Enterprise security & compliance
Governance controls, audit logs, and access policies on every request.
Knowledge access with strict controls
Make organizational knowledge usable while inheriting your existing access rules.
Reach
One AI and context layer
A single layer across the organization in place of disconnected tools.
Connects to your stack
Integrations for CRMs, databases, knowledge bases, and internal tools.
One interface to query and act
Employees query, analyze, automate, and take action across the company ecosystem from one place.
Any frontier model, no lock-in
Run Claude, GPT, Gemini, Llama, or your own model, and switch any time.
Configurable per team
Workflows, permissions, and routing rules tuned to each organization.
AI enablement for your people
Train employees to use LLMs well, so the productivity gain actually lands.
An honest comparison
The gateways are strong on routing and governance today. Where most tools are thin is org-system integration and an interface non-builders actually use. That is the gap this layer targets.
| Capability | Portkey | Helicone | LangChain | LlamaIndex | Pinecone | Clientell |
|---|---|---|---|---|---|---|
| Cost routing + caching | Yes | Yes | Partial | No | No | Yes |
| Governance (RBAC / audit / PII / residency) | Yes | Partial | Partial | Partial | Yes | Yes |
| Org-system integration (CRM / DB / tools / KB) | Partial | No | Partial | Partial | No | Yes |
| Employee query + action interface | No | No | Partial | No | No | Yes |
| Any frontier model, no lock-in | Yes | Yes | Yes | Partial | n/a | Yes |
| Employee AI enablement | No | No | No | No | No | Yes |
- Clientell's cost lever is vendor-backed math, not yet a customer-validated number.
- Clientell covers governance and enablement across the harness; maturity continues to deepen with each release.
- Sources: each vendor's own public site and pricing, captured June 2026.
One layer, three jobs to defend
The same harness answers to engineering, finance, and security.
Engineering & AI leads
Ship reliable agents without hand-rolling routing and fallbacks.
- Agents are hard to debug across long context and many tools.
- Model and provider sprawl, with pressure to avoid lock-in.
- Quality drifts and regresses quietly between releases.
One routing and fallback layer with bring-your-own-models, context accounting and trimming, and quality gates so a cheaper model runs only when it still passes.
CFO
See where the AI budget goes and cut the waste, with the math shown.
- Inference is the largest, fastest-growing line in the AI budget.
- Teams overspend by reprocessing context, retrying, and oversizing models.
- No per-team cost visibility or budget controls.
Routing, caching, and batching with vendor-documented savings, framed as up to 50% and sized to your workload in a calculator, not asserted as a flat number.
CIO
Govern every model call without slowing teams down.
- New exposure: PII leakage, prompt injection, data residency.
- Shadow AI: ungoverned model use with no audit trail.
- Tension between making knowledge accessible and keeping access control.
Governance built into the layer: audit logs, PII screening, role-based access, and a deployment path that respects data residency, with knowledge access that inherits existing controls.
Speaks every model,
plugs into your stack
Run any frontier model and connect the systems you already use. No rewrites, no lock-in.
Connects to the systems you already run
The harness sits over your stack and routes work to the right system, with access controls intact.
- Salesforce
- HubSpot
- Snowflake
- BigQuery
- Postgres
- Confluence
- Notion
- Google Drive
- GitHub
- Jira
- Slack
- Teams
Connector availability is confirmed per organization. The existing Clientell product already operates inside Salesforce; the others name the integration surface this layer targets.
One layer over your models and your systems
A harness starts as routing and caching. It becomes a context layer: the single place where your people, your models, and your systems meet, with governance in the middle of every request.
One interface to query, analyze, automate, and act.
- A separate tool and login for each model
- No shared audit trail across teams
- Spend scattered across projects and bills
- Knowledge locked inside disconnected systems
- One interface over every model and system
- Every call logged, PII-screened, access-checked
- One place to see spend and cut it
- Knowledge reachable, with your controls intact
Where this is going
The goal is to become the intelligent context layer for every enterprise. Secure, deeply integrated, sitting on top of frontier models and your company's systems. One intelligence layer over disconnected tools, so teams get faster decisions, better insights, lower AI costs, and higher productivity.
Questions buyers ask first
See your own number, not ours
Bring one real workload to a 30-minute architecture review. We map where routing, caching, and batching apply and estimate your savings on the spot.