LLM Integration

Your product.
Powered by any LLM.

We wire language models into your existing stack the right way - with caching, fallbacks, resource management, and observability baked in from the start.

Get started See examples

GPT-4o

Supported

Claude 3.5

Supported

Gemini 1.5

Supported

Llama 3

Supported

Integration quality

What "proper" integration means

What most teams build first

✕Hardcoded prompts scattered across the codebase
✕No fallback if the primary provider is down
✕Zero resource tracking until the first surprise bill
✕No caching - every identical query hits the API
✕No eval suite - regressions are caught by users
✕PII sent directly to third-party APIs

What we build instead

✓Versioned prompt registry with templating and tests
✓Multi-provider fallback with automatic retry logic
✓Per-request resource attribution and usage alerts
✓Semantic caching layer - repeat queries return instantly
✓Golden eval suite run on every deployment
✓PII scrubbing before any data leaves your environment

Scope

What we integrate

Completion APIs

Integrate chat and completion endpoints from any provider - with streaming, function calling, structured outputs, and response parsing.

Example use cases

·AI writing assistants
·Summarisation features
·Classification endpoints

Embedding APIs

Build semantic search, similarity scoring, and retrieval layers using embedding models from OpenAI, Cohere, or open-source alternatives.

Example use cases

·Semantic search
·Recommendation engines
·Document clustering

Fine-tuned Models

Integrate custom fine-tuned models trained on your domain data - with hosting, versioning, and A/B testing against base models.

Example use cases

·Domain-specific copilots
·Custom classification
·Brand-voice generation

Technical depth

What we think through so you don't have to

Caching strategy

Exact-match caching for deterministic prompts. Semantic caching (via embedding similarity) for conversational queries. We measure cache hit rates and tune thresholds per use case.

Fallback logic

Primary → secondary → tertiary model routing with configurable thresholds. Automatic retries with exponential backoff. Graceful degradation when all providers are degraded.

Resource management

Token budget enforcement per feature, per user, and per day. Real-time alerting when usage trends exceed forecast. Model routing that switches to efficient models for simple tasks.

Latency optimisation

Streaming responses for UX responsiveness. Pre-warming, connection pooling, and region selection for P95 latency targets. Prompt compression where context permits.

Security & PII

PII detection and scrubbing before payloads leave your network. Secrets management for API keys. Audit logging for every inference call. GDPR-compliant data handling by design.

How we work

Integration process

Audit your stack

We map your existing infrastructure, APIs, and data flows to design an integration that fits - not one that fights your architecture.

Design the layer

We design the integration layer: prompt management, caching, fallback routing, auth, and the observability hooks before writing a line of production code.

Build with evals

Development is test-driven from the start. Every prompt and routing decision has a corresponding eval case that gates deployment.

Ship with monitoring

Production launch includes a live dashboard for usage, latency, error rates, and model performance. Nothing goes dark after handover.

Compatibility

We work with your stack

Node.jsPythonFastAPINext.jsRailsGoJava (Spring)AWS LambdaVercelDockerKubernetesSupabasePostgreSQLRedisCloudflare WorkersAzure FunctionsGCP Cloud RunTypeScript SDKLangChainLlamaIndex

The integration we inherit vs. the one we build

naive_integration.py

import openai
 
PROMPT = "Summarise this: " + user_input
 
def summarise(text):
    res = openai.chat.complete(model="gpt-4",
        messages=[{"role":"user","content":PROMPT}])
    return res.choices[0].message.content
 
# No caching. No fallback. No resource tracking.
# Prompt hardcoded. PII not scrubbed.

production_integration.py

from llm_layer import LLMClient, PromptRegistry
from guards import scrub_pii, validate_output
 
client = LLMClient(
    primary="gpt-4o", fallback="claude-3-haiku",
    cache=SemanticCache(threshold=0.92),
    budget=TokenBudget(max_daily_usd=50)
)
 
# Versioned prompts. PII scrubbed.
# Fallback routing. Usage tracked per call.

Prompting strategy

Choosing the right prompting approach

Approach

Accuracy

Usage

Latency

Maintainability

Zero-shot prompting

⬤⬤○○

⬤⬤⬤⬤

Few-shot prompting

⬤⬤⬤○

Chain-of-thought

⬤⬤⬤⬤

⬤⬤○○

Function calling / structured output

⬤⬤⬤⬤

⬤⬤⬤○

⬤⬤⬤⬤

Fine-tuned model

⬤⬤⬤⬤

⬤⬤○○

⬤⬤⬤⬤

⬤⬤○○

Client result

"Semantic caching alone cut our monthly OpenAI usage from 28,000 to 9,200 - with zero changes to user-facing features."

67%

Usage reduction

58%

Cache hit rate after tuning

2.4×

Faster P95 response time

Cost optimisation

The four levers that reduce LLM cost

Applied in sequence, these interventions typically reduce monthly LLM spend by 50–70% without degrading output quality.

01 Semantic caching

30–50% reduction

Cache responses by semantic similarity, not exact string match. Repeat queries with slightly different wording return immediately and incur zero API cost.

QdrantRedisGPTCache

02 Model routing

20–40% reduction

Route simple tasks to smaller, cheaper models (GPT-3.5, Haiku) and reserve frontier models for complex reasoning. Most tasks don't need GPT-4.

LiteLLMCustom routerOpenRouter

03 Prompt compression

10–25% reduction

Compress system prompts and long context windows using summarisation, selective retrieval, and token-efficient templates without sacrificing output quality.

LLMLinguaCustom summariser

04 Batch processing

Up to 50% reduction

For non-real-time workloads, use batch API endpoints at half the cost. Async queues decouple user latency from generation latency.

OpenAI Batch APICelerySQS

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

LLM Integration

Your product.
Powered by any LLM.

We wire language models into your existing stack the right way - with caching, fallbacks, resource management, and observability baked in from the start.

Get started See examples

GPT-4o

Supported

Claude 3.5

Supported

Gemini 1.5

Supported

Llama 3

Supported

Integration quality

What "proper" integration means

What most teams build first

✕Hardcoded prompts scattered across the codebase
✕No fallback if the primary provider is down
✕Zero resource tracking until the first surprise bill
✕No caching - every identical query hits the API
✕No eval suite - regressions are caught by users
✕PII sent directly to third-party APIs

What we build instead

✓Versioned prompt registry with templating and tests
✓Multi-provider fallback with automatic retry logic
✓Per-request resource attribution and usage alerts
✓Semantic caching layer - repeat queries return instantly
✓Golden eval suite run on every deployment
✓PII scrubbing before any data leaves your environment

Scope

What we integrate

Completion APIs

Integrate chat and completion endpoints from any provider - with streaming, function calling, structured outputs, and response parsing.

Example use cases

·AI writing assistants
·Summarisation features
·Classification endpoints

Embedding APIs

Build semantic search, similarity scoring, and retrieval layers using embedding models from OpenAI, Cohere, or open-source alternatives.

Example use cases

·Semantic search
·Recommendation engines
·Document clustering

Fine-tuned Models

Integrate custom fine-tuned models trained on your domain data - with hosting, versioning, and A/B testing against base models.

Example use cases

·Domain-specific copilots
·Custom classification
·Brand-voice generation

Technical depth

What we think through so you don't have to

Caching strategy

Exact-match caching for deterministic prompts. Semantic caching (via embedding similarity) for conversational queries. We measure cache hit rates and tune thresholds per use case.

Fallback logic

Primary → secondary → tertiary model routing with configurable thresholds. Automatic retries with exponential backoff. Graceful degradation when all providers are degraded.

Resource management

Token budget enforcement per feature, per user, and per day. Real-time alerting when usage trends exceed forecast. Model routing that switches to efficient models for simple tasks.

Latency optimisation

Streaming responses for UX responsiveness. Pre-warming, connection pooling, and region selection for P95 latency targets. Prompt compression where context permits.

Security & PII

PII detection and scrubbing before payloads leave your network. Secrets management for API keys. Audit logging for every inference call. GDPR-compliant data handling by design.

How we work

Integration process

Audit your stack

We map your existing infrastructure, APIs, and data flows to design an integration that fits - not one that fights your architecture.

Design the layer

We design the integration layer: prompt management, caching, fallback routing, auth, and the observability hooks before writing a line of production code.

Build with evals

Development is test-driven from the start. Every prompt and routing decision has a corresponding eval case that gates deployment.

Ship with monitoring

Production launch includes a live dashboard for usage, latency, error rates, and model performance. Nothing goes dark after handover.

Compatibility

We work with your stack

Node.jsPythonFastAPINext.jsRailsGoJava (Spring)AWS LambdaVercelDockerKubernetesSupabasePostgreSQLRedisCloudflare WorkersAzure FunctionsGCP Cloud RunTypeScript SDKLangChainLlamaIndex

The integration we inherit vs. the one we build

naive_integration.py

import openai
 
PROMPT = "Summarise this: " + user_input
 
def summarise(text):
    res = openai.chat.complete(model="gpt-4",
        messages=[{"role":"user","content":PROMPT}])
    return res.choices[0].message.content
 
# No caching. No fallback. No resource tracking.
# Prompt hardcoded. PII not scrubbed.

production_integration.py

from llm_layer import LLMClient, PromptRegistry
from guards import scrub_pii, validate_output
 
client = LLMClient(
    primary="gpt-4o", fallback="claude-3-haiku",
    cache=SemanticCache(threshold=0.92),
    budget=TokenBudget(max_daily_usd=50)
)
 
# Versioned prompts. PII scrubbed.
# Fallback routing. Usage tracked per call.

Prompting strategy

Choosing the right prompting approach

Approach

Accuracy

Usage

Latency

Maintainability

Zero-shot prompting

⬤⬤○○

⬤⬤⬤⬤

Few-shot prompting

⬤⬤⬤○

Chain-of-thought

⬤⬤⬤⬤

⬤⬤○○

Function calling / structured output

⬤⬤⬤⬤

⬤⬤⬤○

⬤⬤⬤⬤

Fine-tuned model

⬤⬤⬤⬤

⬤⬤○○

⬤⬤⬤⬤

⬤⬤○○

Client result

"Semantic caching alone cut our monthly OpenAI usage from 28,000 to 9,200 - with zero changes to user-facing features."

67%

Usage reduction

58%

Cache hit rate after tuning

2.4×

Faster P95 response time

Cost optimisation

The four levers that reduce LLM cost

Applied in sequence, these interventions typically reduce monthly LLM spend by 50–70% without degrading output quality.

01 Semantic caching

30–50% reduction

Cache responses by semantic similarity, not exact string match. Repeat queries with slightly different wording return immediately and incur zero API cost.

QdrantRedisGPTCache

02 Model routing

20–40% reduction

Route simple tasks to smaller, cheaper models (GPT-3.5, Haiku) and reserve frontier models for complex reasoning. Most tasks don't need GPT-4.

LiteLLMCustom routerOpenRouter

03 Prompt compression

10–25% reduction

Compress system prompts and long context windows using summarisation, selective retrieval, and token-efficient templates without sacrificing output quality.

LLMLinguaCustom summariser

04 Batch processing

Up to 50% reduction

For non-real-time workloads, use batch API endpoints at half the cost. Async queues decouple user latency from generation latency.

OpenAI Batch APICelerySQS

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

LLM Integration

Your product.
Powered by any LLM.

We wire language models into your existing stack the right way - with caching, fallbacks, resource management, and observability baked in from the start.

Get started See examples

GPT-4o

Supported

Claude 3.5

Supported

Gemini 1.5

Supported

Llama 3

Supported

Integration quality

What "proper" integration means

What most teams build first

✕Hardcoded prompts scattered across the codebase
✕No fallback if the primary provider is down
✕Zero resource tracking until the first surprise bill
✕No caching - every identical query hits the API
✕No eval suite - regressions are caught by users
✕PII sent directly to third-party APIs

What we build instead

✓Versioned prompt registry with templating and tests
✓Multi-provider fallback with automatic retry logic
✓Per-request resource attribution and usage alerts
✓Semantic caching layer - repeat queries return instantly
✓Golden eval suite run on every deployment
✓PII scrubbing before any data leaves your environment

Scope

What we integrate

Completion APIs

Integrate chat and completion endpoints from any provider - with streaming, function calling, structured outputs, and response parsing.

Example use cases

·AI writing assistants
·Summarisation features
·Classification endpoints

Embedding APIs

Build semantic search, similarity scoring, and retrieval layers using embedding models from OpenAI, Cohere, or open-source alternatives.

Example use cases

·Semantic search
·Recommendation engines
·Document clustering

Fine-tuned Models

Integrate custom fine-tuned models trained on your domain data - with hosting, versioning, and A/B testing against base models.

Example use cases

·Domain-specific copilots
·Custom classification
·Brand-voice generation

Technical depth

What we think through so you don't have to

Caching strategy

Exact-match caching for deterministic prompts. Semantic caching (via embedding similarity) for conversational queries. We measure cache hit rates and tune thresholds per use case.

Fallback logic

Primary → secondary → tertiary model routing with configurable thresholds. Automatic retries with exponential backoff. Graceful degradation when all providers are degraded.

Resource management

Token budget enforcement per feature, per user, and per day. Real-time alerting when usage trends exceed forecast. Model routing that switches to efficient models for simple tasks.

Latency optimisation

Streaming responses for UX responsiveness. Pre-warming, connection pooling, and region selection for P95 latency targets. Prompt compression where context permits.

Security & PII

PII detection and scrubbing before payloads leave your network. Secrets management for API keys. Audit logging for every inference call. GDPR-compliant data handling by design.

How we work

Integration process

Audit your stack

We map your existing infrastructure, APIs, and data flows to design an integration that fits - not one that fights your architecture.

Design the layer

We design the integration layer: prompt management, caching, fallback routing, auth, and the observability hooks before writing a line of production code.

Build with evals

Development is test-driven from the start. Every prompt and routing decision has a corresponding eval case that gates deployment.

Ship with monitoring

Production launch includes a live dashboard for usage, latency, error rates, and model performance. Nothing goes dark after handover.

Compatibility

We work with your stack

Node.jsPythonFastAPINext.jsRailsGoJava (Spring)AWS LambdaVercelDockerKubernetesSupabasePostgreSQLRedisCloudflare WorkersAzure FunctionsGCP Cloud RunTypeScript SDKLangChainLlamaIndex

The integration we inherit vs. the one we build

naive_integration.py

import openai
 
PROMPT = "Summarise this: " + user_input
 
def summarise(text):
    res = openai.chat.complete(model="gpt-4",
        messages=[{"role":"user","content":PROMPT}])
    return res.choices[0].message.content
 
# No caching. No fallback. No resource tracking.
# Prompt hardcoded. PII not scrubbed.

production_integration.py

from llm_layer import LLMClient, PromptRegistry
from guards import scrub_pii, validate_output
 
client = LLMClient(
    primary="gpt-4o", fallback="claude-3-haiku",
    cache=SemanticCache(threshold=0.92),
    budget=TokenBudget(max_daily_usd=50)
)
 
# Versioned prompts. PII scrubbed.
# Fallback routing. Usage tracked per call.

Prompting strategy

Choosing the right prompting approach

Approach

Accuracy

Usage

Latency

Maintainability

Zero-shot prompting

⬤⬤○○

⬤⬤⬤⬤

Few-shot prompting

⬤⬤⬤○

Chain-of-thought

⬤⬤⬤⬤

⬤⬤○○

Function calling / structured output

⬤⬤⬤⬤

⬤⬤⬤○

⬤⬤⬤⬤

Fine-tuned model

⬤⬤⬤⬤

⬤⬤○○

⬤⬤⬤⬤

⬤⬤○○

Client result

"Semantic caching alone cut our monthly OpenAI usage from 28,000 to 9,200 - with zero changes to user-facing features."

67%

Usage reduction

58%

Cache hit rate after tuning

2.4×

Faster P95 response time

Cost optimisation

The four levers that reduce LLM cost

Applied in sequence, these interventions typically reduce monthly LLM spend by 50–70% without degrading output quality.

01 Semantic caching

30–50% reduction

Cache responses by semantic similarity, not exact string match. Repeat queries with slightly different wording return immediately and incur zero API cost.

QdrantRedisGPTCache

02 Model routing

20–40% reduction

Route simple tasks to smaller, cheaper models (GPT-3.5, Haiku) and reserve frontier models for complex reasoning. Most tasks don't need GPT-4.

LiteLLMCustom routerOpenRouter

03 Prompt compression

10–25% reduction

Compress system prompts and long context windows using summarisation, selective retrieval, and token-efficient templates without sacrificing output quality.

LLMLinguaCustom summariser

04 Batch processing

Up to 50% reduction

For non-real-time workloads, use batch API endpoints at half the cost. Async queues decouple user latency from generation latency.

OpenAI Batch APICelerySQS

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in Touch See Our Work

What happens next

Send us a message

Tell us what you're building or what's broken.

Discovery call (30 min)

We ask hard questions. You get honest answers.

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Your product.
Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build your
next digital product?

Tell us about
your project

Your product.
Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build your
next digital product?

Tell us about
your project

Your product.
Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build your
next digital product?

Tell us about
your project

Your product.Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build yournext digital product?

Tell us aboutyour project

Your product.Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build yournext digital product?

Tell us aboutyour project

Your product.Powered by any LLM.

What "proper" integration means

What we integrate

What we think through so you don't have to

Integration process

We work with your stack

The integration we inherit vs. the one we build

Choosing the right prompting approach

The four levers that reduce LLM cost

Ready to build yournext digital product?

Tell us aboutyour project

Your product.
Powered by any LLM.

Ready to build your
next digital product?

Tell us about
your project

Your product.
Powered by any LLM.

Ready to build your
next digital product?

Tell us about
your project

Your product.
Powered by any LLM.

Ready to build your
next digital product?

Tell us about
your project