Service
Agentic AI Development
Autonomous AI agents that plan, use tools, and complete real work — built to run reliably in production, not just demo well
Why work with us on agentic ai development
Agentic AI is the step past a chatbot: systems that decide what to do next, call your APIs and databases, verify their own output, and hand off to a human when they should. We build these end to end — orchestration and planning, retrieval over your data, tool and function calling, memory, guardrails, evaluation harnesses, and the observability you need to trust them in production. We work across the frontier model APIs and open-source models you can host yourself, and we will tell you honestly when a deterministic workflow beats an agent.
Multi-agent orchestration with supervisor and hierarchical patterns
Tool and function calling against your APIs, databases, and internal systems
RAG pipelines with hybrid search, re-ranking, and grounded citations
Model Context Protocol (MCP) servers and clients for reusable tool layers
Short-term, long-term, and episodic agent memory
Human-in-the-loop approval gates for high-stakes actions
Guardrails, prompt-injection defence, and output validation
Evaluation harnesses, tracing, and cost/latency observability
Fine-tuning and self-hosted open-source model deployment
Get started
Technical proposal call
Scope and timeline agreed
Development and delivery
Stack
Technologies we use
Agent Frameworks & Orchestration
The planning and coordination layer — how agents decide what to do, call tools, hand off to each other, and recover when a step fails.
LangGraph
Orchestration
LangChain
Agent Framework
Claude Agent SDK
Agent Framework
OpenAI Agents SDK
Agent Framework
CrewAI
Multi-Agent
Microsoft AutoGen
Multi-Agent
LlamaIndex
Data Framework
Pydantic AI
Typed Agents
Semantic Kernel
Orchestration
Model Context Protocol
Tool Protocol
Temporal
Durable Workflows
n8n
Workflow Automation
LLM Model APIs
Frontier models via managed APIs and enterprise gateways. We stay model-agnostic and route each task to the model that handles it best on quality, latency, and cost.
Anthropic Claude API
Claude Opus / Sonnet / Haiku
OpenAI API
GPT & o-series
Google Gemini API
Gemini Pro / Flash
Amazon Bedrock
Multi-Model Gateway
Azure OpenAI
Enterprise Hosting
Google Vertex AI
Managed Platform
Mistral AI API
Model Provider
Cohere API
Embed & Rerank
Groq
Low-Latency Inference
Together AI
Open Model Hosting
Fireworks AI
Open Model Hosting
OpenRouter
Model Routing
Open-Source Models & Self-Hosted Serving
When data cannot leave your network or per-token pricing does not fit, we deploy open-weight models on your own infrastructure with production-grade inference servers.
Llama
Meta – General Purpose
Qwen
Alibaba – Multilingual
DeepSeek
Reasoning & Code
Mistral & Mixtral
Efficient MoE
Gemma
Google – Lightweight
Phi
Microsoft – Small Models
Falcon
TII – General Purpose
Whisper
Speech-to-Text
vLLM
Inference Server
Ollama
Local Inference
llama.cpp
Quantised Inference
Hugging Face TGI
Inference Server
RAG, Vector Search & Memory
Retrieval that grounds every answer in your own content — hybrid search, re-ranking, document parsing, and the memory layer agents use across sessions.
pgvector
Postgres Vectors
Pinecone
Vector Database
Qdrant
Vector Database
Weaviate
Vector Database
Chroma
Vector Database
Milvus
Vector Database
Elasticsearch
Hybrid Search
Redis
Cache & Memory
Neo4j
Graph RAG
Unstructured
Document Parsing
LlamaParse
Document Parsing
Cohere Rerank
Re-ranking
Evaluation, Observability & Guardrails
The difference between a demo and a production system: measurable accuracy, full tracing, output validation, cost controls, and regression tests on every prompt change.
LangSmith
Tracing & Eval
Langfuse
Observability
Arize Phoenix
LLM Observability
Ragas
RAG Evaluation
DeepEval
LLM Testing
Guardrails AI
Output Validation
NeMo Guardrails
Safety Policies
OpenTelemetry
Instrumentation
Weights & Biases
Experiment Tracking
LiteLLM
Model Gateway
Helicone
Cost Monitoring
Docker & Kubernetes
Deployment
Fine-Tuning & Model Adaptation
When prompting and retrieval are not enough, we adapt open-weight models to your domain with parameter-efficient training on your data.
LoRA & QLoRA
Parameter-Efficient
PEFT
Hugging Face
Unsloth
Fast Fine-tuning
Axolotl
Training Framework
TRL & DPO
Preference Tuning
DeepSpeed
Distributed Training
PyTorch
Deep Learning
Transformers
Model Library
Architecture
Agent patterns we build
We pick the simplest topology that solves your problem. Complexity is a cost, and most production agents are smaller than the demos suggest.
Single agent with tools
One model with a well-scoped toolbelt — the right starting point for most problems. Fast, cheap, and far easier to evaluate than a swarm.
Supervisor / worker
A routing agent delegates to specialists and merges their output. Keeps each sub-agent focused and its context small.
Sequential pipeline
Deterministic stages where each step feeds the next — retrieval, draft, critique, revise. Predictable and simple to debug.
Parallel fan-out
Many agents work independently and results are merged. Cuts wall-clock time on research, review, and large migrations.
Reflection & critique
The agent reviews its own work — or an adversarial second agent tries to refute it — before anything is committed.
Human-in-the-loop
Approval gates on high-stakes actions. The run pauses, a person signs off, and execution resumes with full context intact.
Applications
What teams use agents for
Customer support agents
Resolve tier-one volume end to end against your docs and order systems, escalating with full context when confidence drops.
Internal research assistants
Agents that search across your wikis, tickets, and databases, then return a cited answer instead of a list of links.
Document & claims processing
Extract, validate, and route structured data from contracts, invoices, and forms with a human review queue for edge cases.
Sales and CRM automation
Enrich leads, draft tailored outreach, log activity, and keep your CRM current without manual data entry.
Developer productivity agents
Code review, migration, test generation, and triage agents wired into your repos and CI through MCP tool layers.
Operations copilots
Agents that monitor systems, investigate anomalies, draft incident summaries, and execute approved runbook steps.
FAQ
Questions we get asked
What makes an agent different from a chatbot?
A chatbot answers. An agent decides what to do next, calls your APIs and databases to actually do it, checks the result, and either continues or escalates. The engineering effort sits in the tool layer, the guardrails, and the evaluation harness — not the prompt.
Which model should we use?
It depends on the task, and often several are right for different steps. We benchmark candidates on your real data and route per task — a frontier model for hard reasoning, a small fast one for classification, an open-weight model where data cannot leave your network. We build model-agnostic so you are never locked in.
Can we run everything on our own infrastructure?
Yes. We deploy open-weight models such as Llama, Qwen, and Mistral on your hardware or VPC using vLLM, Ollama, or TGI, with the same orchestration and evaluation layer we would use against a hosted API.
How do you stop agents from hallucinating or going off the rails?
Grounding every answer in retrieved content, validating structured output against schemas, constraining what tools can do, adding approval gates on destructive actions, and running an evaluation suite on every prompt change. We also defend against prompt injection where agents read untrusted input.
How do you control cost and latency?
Model routing, prompt caching, context trimming, batching, and using smaller models for the steps that do not need a large one. We instrument token spend per run from day one so cost is a number you can see, not a surprise on the invoice.
What if an agent is the wrong solution?
We will say so. Plenty of problems are better served by a deterministic pipeline with one LLM call in the middle — cheaper, faster, and far easier to test. We would rather build you the right thing than the fashionable one.
How we work
Our development process
Use Case & Feasibility
We map the workflow, define success metrics, and tell you honestly whether an agent or a deterministic pipeline is the right tool.
1-2 weeks
Architecture & Prototype
Model selection, orchestration design, tool contracts, and a working prototype benchmarked on your real data.
2-4 weeks
Build & Evaluate
Production build with an evaluation harness, guardrails, memory, tracing, and regression tests on every prompt change.
4-10 weeks
Deploy & Operate
Rollout with cost controls, latency budgets, monitoring, and a feedback loop that improves the agent after launch.
1-2 weeks
Why us
Benefits of working with us
Engineers who have shipped agents to production, not just prototypes
Model-agnostic — we pick the right model per task and avoid lock-in
Self-hosted open-source options when data cannot leave your network
Evaluation-first: every agent ships with tests and measurable accuracy
Cost and latency engineering, including routing and caching strategy
Honest advice when a simpler non-agentic solution will serve you better
Ready to hire our agentic ai development experts?
A senior engineer replies within one business day with a real technical read on your project.