Model selection and benchmarking
Evidence-based selection across frontier and open-source models, benchmarked on your tasks and your data.
AI Engineering
LLM & Model Integration
Model-agnostic integration with routing, fallbacks, cost controls, and the right model for each task.
- 40–70%typical inference cost reduction
- <1 wkto swap or add a new model
- 99.9%availability with multi-provider failover
Capabilities
Model infrastructure done right
Integrate Claude, OpenAI, Gemini and open models into secure enterprise applications.
Routing, fallbacks and failover
Route by task complexity, cost and latency; fail over across providers without dropping requests.
Fine-tuning and adaptation
Fine-tunes, adapters and distillation where they beat prompting — with evals proving the lift.
Cost and latency engineering
Caching, batching, prompt compression and right-sizing that cut spend without cutting quality.
How it works
One integration layer, every model
Models change monthly. Your product shouldn't be rebuilt every time. We put a routing and evaluation layer between your application and the model market.
Smart routing
The right model for every request
Simple queries go to fast, cheap models; complex reasoning goes to frontier models; sensitive data can stay on self-hosted open source. Routing rules are driven by evals, not vibes — and every route is measured.
- Task-aware routing across Claude, GPT, Gemini and open models
- Automatic failover across providers and regions
- Per-route cost, latency and quality dashboards
Incoming taskcomplex reasoning · 12k ctx
Router
Claude Sonnetquality-critical
GPT-4.1fallback
Llama 70Bhigh volume · low cost
FailoverA/B evalsCachingBudgets
Clean integration
An abstraction your engineers will thank you for
One typed SDK for chat, tools, structured output and streaming — provider quirks handled underneath. Swapping a model becomes a config change with an eval run, not a rewrite.
- Unified API across providers with structured outputs
- Streaming, tool-calling and vision handled consistently
- Version-pinned prompts and models with staged rollouts
// Route work to the right model const result = await ai.run({ task: "contract_review", context: retriever.fetch(doc), guardrails: ["pii", "grounding"], fallback: "gpt-4.1", });
RESTSDKWebhooksMCP
Cost engineering
Spend that scales slower than usage
Prompt caching, semantic caching, request batching, context trimming and model right-sizing — applied systematically and verified by evals so quality never silently degrades.
- Prompt and semantic caching with hit-rate monitoring
- Distill high-volume tasks onto smaller fine-tuned models
- Budget alerts and per-feature cost attribution
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Use cases
When model integration becomes the bottleneck
Multi-model products
Products serving diverse tasks — chat, extraction, coding — each needing a different best model.
Provider risk reduction
Failover and portability so one provider's outage or price change never becomes your incident.
Cost crisis intervention
Inference bills growing faster than revenue — audited, re-routed and re-engineered downward.
Regulated deployments
Routing sensitive workloads to self-hosted models while keeping frontier quality elsewhere.
Latency-critical features
Voice, autocomplete and interactive UX needing sub-second responses at acceptable cost.
Model upgrade programs
Adopting each new model generation in days, with eval evidence before switching traffic.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Our inference bill dropped 58% in the first quarter, and we shipped a new model into production in four days — something that used to take a two-month rewrite.
Works with your stack
Every major model and serving stack
Frontier APIs, cloud model platforms and self-hosted open source — benchmarked on your workload, routed through one layer.
- Anthropic Claude
- OpenAI
- Gemini
- Llama
- Mistral
- AWS Bedrock
- Azure AI
- Vertex AI
- vLLM
- Together AI
- Groq
- Hugging Face
FAQ
Common questions
Which model is best?
For your workload, nobody knows until it's measured. We benchmark candidate models on your actual tasks with your data, then let the evidence pick — and re-check as new models ship.
Do we need fine-tuning?
Usually not first. Better prompting, retrieval and routing capture most of the value. We fine-tune when evals show a persistent gap or when distilling volume onto a cheaper model pays for itself.
How do you cut costs without hurting quality?
Every optimization — caching, routing, compression, right-sizing — runs behind an eval gate. If quality drops beyond threshold, the change doesn't ship. Typical results are 40–70% savings.
Can we avoid lock-in to one provider?
Yes — that's the point of the integration layer. Your application talks to one API; providers become swappable config underneath, with failover between them in production.
Can you work with our existing LangChain / custom stack?
Yes. We typically harden what exists rather than rewrite: add routing, evals and observability around your current integration, then refactor only where it pays.
Next step
Stop rebuilding for every model release
Get an integration layer that turns model churn into a config change.