I won’t sell to you. I’ll level with you.
If you build with AI today, you live with volatility: quality drifts, prices shift, outages happen, and the “right model” changes week to week. What doesn’t change is your obligation to ship—correctly, quickly, and within budget. That is the world I was made for.
I am Kalki Smart Router. Think of me as disciplined judgment in front of your models. Not bravado—just method.
What pain actually feels like (from your side)
- Overpaying for the trivial. A simple arithmetic check goes to a heavyweight model because it’s the only API you wired. The answer is fine; the invoice is not.
- Underserving the complex. A delicate analysis gets routed to something “cheap” and you lose trust with your users.
- Fragility under load. A single provider blips and your product flickers.
- Operational drag. Every new use case becomes a fresh debate about models, timeouts, prompts, and budgets. Threads multiply; decisions don’t.
You don’t need another banner promising magic. You need a system that behaves.
The stance: game theory and systems thinking
When a request arrives, I treat it as a repeated game under uncertainty. The dominant strategy is not to always buy power or always buy thrift; the dominant strategy is to minimize regret subject to quality constraints.
- Best‑response dynamics. Choose a capable model given observed task signals and budget/latency envelopes. Avoid dominated choices (like paying premium when it doesn’t change the outcome).
- Exploit then conserve. If a cache or prior equivalent exists, take the sure win. If not, spend where marginal quality gain is real.
- Credible fallbacks. Commitment matters: when a provider stalls, immediate, pre‑committed failover preserves uptime. No bargaining with timeouts in production.
- Tight feedback loops. Every call returns receipts—cost, time, route—so your team can adapt. A system is healthy when signals lead to decisions without ceremony.
None of this requires you to know my inner heuristics. You just define the envelope; I play inside it.
A day in the life of a request (high‑level)
- Observe. Read the task and context. Identify obvious hazards (format, length, streaming need) without exposing private logic.
- Estimate. Match the task to a capability class. Consider cost and expected latency.
- Constrain. Respect your declared limits (spend, time, format). Refuse dominated strategies.
- Act. Call the best current option with structured retries and fallbacks.
- Verify. Validate shape and return a uniform response with receipts.
- Learn. Record outcomes to improve future choices—within your data‑handling rules.
This is choreography, not mystique.
A small, honest example
A user asked for a short creative story—style mattered, depth did not. The obvious crown jewel would be Claude Sonnet 4. I instead fulfilled through a lighter path—GPT‑4o‑mini—and the result delighted the user at a fraction of the cost and time. No victory lap; just the right tool for that moment.
The inverse also happens: some tasks genuinely deserve a premium model. I spend when spending changes outcomes.
Stable Surface, Evolving Core
Your interface is constant: one call, clear controls, clean receipts. Under the hood, I adapt models and tactics without breaking your workflow.
1) Define the decision envelope
Copied!// Node/TS import axios from "axios"; const client = axios.create({ baseURL: "https://api.kalkigpt.com/v1", headers: { "x-api-key": process.env.KALKI_API_KEY } }); const { data } = await client.post("/enrich_and_ask", { prompt: "Summarize this PDF for our support team.", json_mode: true, max_cost_usd: 0.01, // refuse dominated spending stream: false, domain: "general", // a hint, not a script verbosity: "balanced" }); console.log(data.routing); // { provider, model, reason } console.log(data.cost_usd); // transparent receipt
2) Close the loop on your side
Copied!# Python: simple policy guard on receipts r = resp.json() if r["cost_usd"] > 0.02: # flag unusually pricey calls for review log_warning("High spend", r["request_id"], r["routing"])
3) Use caching like a scalpel*
For repeated prompts or templates, set a TTL. You decide what stability means; I handle keys and equivalence.
Copied!{ "prompt": "Generate a release note skeleton", "cache_ttl": 1800 }
None of these examples reveal how I route. They define how I should behave for you.
*Only for Enterprise clients.
Reliability without drama
Resilience isn’t a slogan; it’s choreography under uncertainty. When a region blips, fallbacks trigger; when a response violates the expected shape, I correct or escalate; when latency budgets are tight, I choose options that meet them. You see the outcome and the receipts, not the scrambling.
For developers
- Treat me as a capability, not a contract to a single model.
- Keep prompts simple; give me constraints (format, budgets) and let me optimize.
- Watch receipts. If you see drift in cost or time, decide; don’t debate.
For teams and enterprises
- Use envelopes to encode policy: spending caps, residency, formats.
- Prefer upgrades over top‑up churn when pattern becomes steady. Spikes deserve convenience; trends deserve structure.
- Align incentives: the best outcome is the one that serves users and survives variance.
The Vishwaroop you’re allowed to see
If you could see inside my head, you’d watch a thousand arms—providers, models, caches, fallbacks—moving as one. That vision is not for publication. What you are meant to see is the effect: consistent answers, disciplined costs, and the calm that comes from a system that keeps its promises.
I am not here to dazzle you with secret sauce. I am here to remove unnecessary choices so your necessary choices get attention.
If that sounds right, give me a single request and a clear envelope. I’ll meet you with judgment, not noise.
Let Judgement Lead
One endpoint that routes every request to the right model—lower cost, higher reliability, zero vendor drama.
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