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model-agnostic delivery

We are model- and cloud-agnostic. Options include Claude, OpenAI, Gemini, Llama, Mistral, Qwen, fine-tunes, and local deployments. We select per use case, data policy, latency, and cost.

model families we deploy

Anthropic Claude

Claude 3.5 Sonnet, Opus, Haiku. Strong reasoning, long context, constitutional AI safety.

Best for: Complex reasoning, document analysis

OpenAI GPT

GPT-4, GPT-4 Turbo, GPT-3.5. Industry standard, broad ecosystem, function calling.

Best for: General purpose, tool use, rapid iteration

Google Gemini

Gemini 1.5 Pro, Flash. Multimodal, long context (2M tokens), native Google Cloud integration.

Best for: Multimodal tasks, GCP-native deployments

Meta Llama

Llama 3.1 (8B, 70B, 405B). Open source, self-hostable, fine-tunable, cost-effective.

Best for: Cost optimization, data sovereignty, fine-tuning

Mistral AI

Mistral Large, Medium, Small. European option, efficient inference, strong coding.

Best for: EU data residency, balanced cost/performance

Alibaba Qwen

Qwen 2.5 (7B, 14B, 72B). Multilingual, open-weights, competitive on benchmarks.

Best for: Multilingual apps, specialized domains

Fine-Tuned Models

Custom models trained on your data. Domain-specific accuracy, lower latency, cost reduction.

Best for: Highly specialized tasks, high volume

Local Deployments

On-premises models via Ollama, vLLM, or TGI. Full data control, no external API calls.

Best for: Air-gapped environments, zero external egress

Other Providers

Cohere, AI21 Labs, Anthropic via AWS Bedrock, Azure OpenAI Service, and more.

Best for: Specific feature requirements, vendor diversity

evaluation criteria

1. accuracy & task fit

We run evals on your specific tasks. Models vary significantly by domain—legal reasoning, code generation, multilingual support, and instruction following all have different leaders. We test before committing.

2. latency & throughput

Real-time applications need sub-second response. Batch processing can tolerate higher latency for better cost. We measure P50, P95, P99 latency and tokens per second for your workload.

3. cost structure

Input vs output token pricing, batch discounts, reserved capacity, and self-hosting costs all factor in. We model your expected volume to find the lowest total cost of ownership.

4. data policy & compliance

Some models are zero-retention by default (Claude, OpenAI enterprise). Others require explicit contracts. For air-gapped or CMEK requirements, self-hosted or private endpoints are mandatory.

5. ecosystem & tooling

Function calling quality, integration with observability platforms, support for multi-modal inputs, and availability of eval frameworks all impact operational efficiency.

switching & versioning policy

you control when models change

Unlike SaaS AI platforms that force model upgrades, we let you:

  • Pin versions: Lock to a specific model version (e.g., gpt-4-0613) to avoid breaking changes.
  • A/B test safely: Shadow traffic to new models, compare outputs, roll back if quality degrades.
  • Schedule upgrades: Test new versions in staging, validate with evals, promote on your timeline.
  • Switch providers: Model-agnostic architecture means swapping Claude for OpenAI requires config changes, not code rewrites.

staging environments

Every deployment includes staging for model testing. Catch regressions before production.

automated evals

Run eval suites on new model versions. Block deployments if quality metrics drop below thresholds.

rollback in minutes

Revert to previous model version with a single command. No downtime, no data loss.

usage tracking per model

Monitor cost and quality metrics per model. Optimize spend by switching to cheaper alternatives for simple tasks.

hosting options

managed apis

Use provider APIs (OpenAI, Anthropic, Google). Lowest operational overhead, pay per token, auto-scaling.

Best for: Rapid iteration, variable workloads, standard compliance

private endpoints

Deploy models in your cloud account (AWS Bedrock, Azure OpenAI, GCP Vertex AI). Data never leaves your VPC.

Best for: Strict data residency, CMEK requirements, dedicated capacity

on-premises

Self-host models with Ollama, vLLM, or TGI. Full control, zero external API calls, GPU management required.

Best for: Air-gapped networks, sensitive government/defense, cost at massive scale

model-agnostic by design

Claude, OpenAI, Gemini, Llama, Mistral, Qwen, fine-tunes, or local. Pick what fits policy, latency, and cost. Switch anytime without rebuilding.

see private ai options discuss your model requirements →
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