annual inference spend returned
SLM Engineering · Services Datasheet
Your agents are built.
Now make them cheaper, faster and yours.
At hundreds of agents in production, every frontier-model call adds cost, latency and a dependency you don't control. Most of those agents do narrow, repetitive work. Evolvable.ai fine-tunes small language models for exactly that work: trained on your own production logs, proven against your real traffic, running on your GPUs, with weights you own.
- 1 · Audit
- 2 · Distil
- 3 · Evaluate
- 4 · Shadow
- 5 · Route
Agent fleet · last 30 daysranked by spend × task narrowness
Ticket triage$38.4k / mo1.2M calls
Email routing$17.2k / mo910k calls
Invoice extraction$21.0k / mo640k calls
Contract negotiationfrontieropen-ended reasoning
500+ agents · 3 picked first
SLM · Ticket triage v3Fine-tuned7B base · LoRA distilled · your GPUs
Eval on your trafficFrontierSLM
Routing accuracy97.4%96.9%
p95 latency2.1 s0.31 s
Cost / 1k calls$3.10$0.42
Arabic test set95.1%92.8%
Arabic test set · v395.1%94.7%
184k logged conversations · data never left your environment
Shadow run
14 days alongside live98.9% agreement with the frontier model on 410k real tickets
Routed
97% handled by SLMLow-confidence calls escalate to the frontier model automatically
Live
Weights handed overServed on your GPUs behind the endpoint your agents already call
≈ 86% lower cost on this agent
What we deliver
Start with three agents. Scale to the fleet.
Model engineering
- Workload auditRank every agent by spend, volume and task narrowness
- Distillation & fine-tuningFrontier behaviour, taught to a small model on your logs
- Arabic & bilingual tuningGulf Arabic and English tested as separate gates
Quality & proof
- Evals on your trafficAccuracy, tool calls and guardrails vs the frontier model
- Shadow runsThe SLM runs beside live traffic before it answers anyone
- Release regressionEvery retrain re-runs the full suite before go-live
Serving & routing
- On-prem & air-gappedServed on your GPUs, private cloud or sealed network
- Confidence routingHard cases escalate to the frontier model automatically
- Sized for your hardwareQuantised to fit the GPUs you already have
Operations
- Cost trackingCost per task, before and after, in your ROI reporting
- Drift monitoringRetrain when your data or business changes
- Full handoverWeights, datasets and pipelines are yours
Built for
01CTO & AI platformLower spend without rebuilding a single agent
02CFO & financeA cost per task you can forecast
03CISO & data officeData and models stay inside your perimeter
04Head of AI opsFaster chains, fewer timeouts
60–80%
lower inference cost on migrated workloads
3–5×
faster responses on narrow, high-volume tasks
0
training records leave your environment
0%
of weights, datasets and pipelines owned by you
Four steps. Models you own.
01Find the agents worth moving.
Workload audit
- Pulls 30 days of call logs, cost and latency for every agent in your OS
- Scores each workload on volume, spend and how narrow the task is
- Delivers 3 ranked candidates with a business case per agent
2 weeks to a ranked shortlist and business case
Monthly inference spend · top agents
Top agents
Ticket triage$38k
Invoice extraction$21k
Email routing$17k
Contract reviewkeep
3 candidates · narrow task, high volume, clean logs
02Your frontier model teaches your small one.
Distil & fine-tune
- Builds training sets from your own production conversations
- Masks PII before anything is trained, on your infrastructure
- Fine-tunes open models in English and Arabic, sized to your GPUs
6–8 wks from logs to a benchmarked pilot model
Distil & fine-tune
184kproduction logs
151kcleaned & labelled
LoRAfine-tune on 7B
v3adapter ready
PII masked before trainingEnglish + Arabicquantised for your GPUs
03Prove it before it answers anyone.
Evaluate & shadow
- Eval suite built on your real traffic: answers, tool calls, guardrails
- Hard release gates against the frontier model, per language
- Shadow runs beside live traffic before any cutover
≤ 1 pt accuracy gap gate against the frontier model
Release gates · v3 vs Frontier
Release gates
Accuracy−0.5 pt · gate 1 pt
Tool-call trajectory99.2% match
Guardrail suite0 new violations
Shadow agreement98.9% · 14 days
04Small model first. Frontier when it matters.
Route & operate
- Confidence-based routing sends hard cases to the frontier model
- Served behind the endpoint your agents already call: no rebuild
- Drift monitoring, scheduled retrains and regression on every release
≈ 85% lower cost per task on routed workloads
100 requests
Your SLM97confidence ≥ 0.82
Frontier model3hard cases only
Illustrative · 20 agents migrated
What it adds up to
faster p95 response on migrated agents
agents on models you own
Figures are directional examples for a fleet of this size, not a guarantee. We baseline your real numbers in the audit.
- You own the weights Models, adapters, datasets and pipelines are handed over in full.
- Data stays with you Training and serving run on your infrastructure, air-gapped if needed.
- Any open model Qwen, Llama, Mistral, Phi, Gemma, Falcon, Jais. No lock-in.
- Fits your OS Drops in behind your existing model endpoint. No agent rebuild.
Take it with you
The SLM engineering datasheet
Share it with your CTO, CFO and AI ops lead before you pick your first three agents.
PDF · 2 pages · 0.6 MB
Bring us your three costliest agents.
See the business case in two weeks.
A two-week workload audit on your agent logs, on your infrastructure. You get three ranked candidates, target cost per task and a pilot plan.