Custom agent design
We map your workflow step by step, decide what the agent owns and what a human keeps, then design the reasoning loop around that split. The map comes before the code, every time.
AI Agents
Agents that finish the job,
not just talk about it
We build autonomous AI agents that log into your tools, follow your rules, and carry a task from the first trigger to the final result. Not a chat window that gives advice. A teammate that does the work and reports back.
LangGraphMCPTool CallingHuman in the Loop
60%
Average manual workload removed
4 to 6
Weeks to a working agent in production
24/7
Uptime with monitoring and alerts
Why It Matters
Most teams do not need more dashboards. They need the work done.
Every growing company hits the same wall. Skilled people spend half their week copying data between systems, chasing updates, writing the same five emails, and cleaning spreadsheets that nobody enjoys owning. Hiring more people scales the cost, not the leverage.
An AI agent changes the shape of that problem. We give it a clear job, a set of tools, a memory of what happened before, and strict limits on what it is allowed to do. It runs on a schedule or on a trigger, it handles the boring ninety percent on its own, and it escalates the strange ten percent to a human with full context attached.
We have shipped agents that qualify inbound leads before a rep wakes up, agents that reconcile invoices across three systems, and agents that keep a knowledge base honest without anyone babysitting them. The pattern is always the same. Start with one painful workflow, prove the result in weeks, then expand.
How we keep it honest
- Built around one measurable workflow, never a vague promise of automation
- Connected to the tools you already pay for, from CRM to inbox to database
- Every action logged, traceable, and reversible so you stay in control
- Guardrails, approvals, and spend limits configured before day one
Capabilities
What we build
Every engagement is scoped to your workflow, but these are the building blocks we bring to the table.
Tool and API orchestration
Agents that call your CRM, your database, your billing system, your internal APIs, and third party services through clean tool definitions and the Model Context Protocol.
Multi agent workflows
A planner that breaks the goal into steps, specialists that execute them, and a reviewer that checks the output before anything ships. Coordinated with LangGraph state machines.
Memory and context
Short term working memory for the task at hand and long term vector memory for everything the agent has learned about your customers, products, and past decisions.
Human in the loop controls
Approval gates on anything sensitive, spend and rate limits, role based permissions, and a clean handoff to a person the moment confidence drops below your threshold.
Evaluation and monitoring
An eval suite that scores agent output against real examples, plus live dashboards, cost tracking, failure alerts, and a full trace of every run for debugging.
How We Work
From first conversation to production
No six month discovery phase. We move in short cycles and you see working software early.
01
Workflow discovery
We sit with the team that actually does the work, watch the process, and pick the single workflow with the best ratio of pain to complexity. You leave this stage with a written scope and a target metric.
02
Architecture and guardrails
We design the agent loop, the tool set, the data access, and the safety rules. You approve exactly what the agent can read, write, and spend before a single line of production code exists.
03
Build and evaluate
We build in short cycles with a growing test set drawn from your real cases. Every release is scored on accuracy, cost per run, and time saved, so progress is visible rather than promised.
04
Pilot with real users
The agent runs beside your team in shadow mode first, then with approvals, then on its own. We tune prompts, tools, and thresholds using what actually happens rather than what we assumed.
05
Deploy, monitor, improve
We ship to your cloud or ours, wire up logging and alerts, hand over documentation, and keep improving the agent while it earns its keep in production.
Tools we build with
LangGraphLangChainModel Context ProtocolOpenAIClaudePythonFastAPINext.jsPostgreSQLRedisDockerAWS
Let's Talk
Pick one painful workflow. We will show you what an agent does with it.
Tell us what your team keeps doing by hand. We will come back with a scoped plan, a realistic timeline, and an honest answer on whether an agent is the right call.
We reply within 24 hours with a clear plan, a timeline, and a number.
Use Cases
Where agents pay for themselves fastest
Patterns we have shipped before, so we already know where the traps are.
Sales research and outreach
The agent researches every inbound lead, enriches it from public and internal sources, scores it against your ideal customer profile, drafts a first touch, and books the meeting in your calendar.
Support triage
Incoming tickets get read, classified, answered from your knowledge base when the answer exists, and routed with a written summary when a human is genuinely needed.
Back office operations
Invoice matching, order reconciliation, data entry across systems, and status chasing. Quiet work that eats hours and never shows up on a roadmap.
Reporting and monitoring
The agent pulls numbers from every system on a schedule, spots what moved, writes the summary a human would have written, and flags the anomalies worth a conversation.
Recruiting and screening
Applications get parsed, matched to the role, ranked with reasoning you can audit, and moved through your pipeline with a short brief for the hiring manager.
Internal copilots
A private agent that answers questions about your own policies, contracts, and data, then takes the follow up action inside your systems rather than leaving it to the reader.
What You Get
Everything handed over, nothing held hostage
You finish the engagement owning the system, the code, and the knowledge to run it without us. That is the point.
- A production agent running in your environment with your credentials and your rules
- Full source code, prompts, tool definitions, and infrastructure configuration
- An evaluation suite built from your real cases so future changes are safe
- Monitoring dashboards for accuracy, cost per run, latency, and failures
- Written documentation plus a live handover session with your team
- Thirty days of post launch support and tuning included as standard
FAQ
AI Agents, answered straight
The questions every serious buyer asks us before the first invoice.
How long does it take to get an agent into production?
Most first agents go live in four to six weeks. Simple, well defined workflows can be running in two. The timeline is driven far more by how clean your data and API access are than by the AI itself.
What stops the agent from doing something harmful?
Three layers. Scoped permissions so it can only touch what you approve, approval gates on any action that costs money or leaves your company, and structured validation on every output before it is used. Everything is logged and reversible.
Which models do you use?
Whichever fits the job. We benchmark the leading models from Anthropic, OpenAI, and open source families against your actual tasks, then pick on accuracy, latency, and cost. The architecture stays model agnostic so you can switch later without a rebuild.
Do we own the code?
Yes. You own the code, the prompts, and the data, and it lives in your repositories and your cloud. There is no lock in and no per seat toll on something you paid us to build.
Can it run entirely on our own infrastructure?
Yes. We deploy into your AWS, Azure, or GCP account, and where data residency or privacy rules demand it, we run open weight models on hardware you control.
What if the workflow changes six months from now?
That is expected. The agent is built from modular tools and prompts with an eval suite behind it, so changes are a small edit and a test run rather than a rewrite. We also offer an ongoing support retainer.
Keep Exploring
Explore the rest of the stack
Most of our work combines two or three of these. Start where the pain is loudest.
Ready to Start?
Let's build the version of this
that actually ships
Tell us the problem in plain language. We will tell you whether it is worth building, what it takes, and what it costs, before you spend a rupee or a dollar.
We reply within 24 hours with a clear plan, a timeline, and a number.