Straight answers, before you talk to anyone.

A chatbot answers. An agent acts: it plans a sequence of steps, calls tools and APIs to change state in your systems, checks the result and either finishes or escalates. Chatbots need good retrieval; agents additionally need permissions, idempotency, budgets and traces.

Three layers: tools are permissioned and typed so an agent can only do what it was granted; irreversible actions pass through a human approval gate; and every run carries hard budget, step and time limits that terminate it. All of it is logged.

A scoped production agent covering one workflow typically takes six to ten weeks including evals and observability. A proof of concept against real data takes two to three weeks.

A forward deployed engineer is a senior software engineer who works inside a customer's business instead of at a distance. They learn how the business really runs, its systems, data, processes and constraints, then design and write the production code that makes a product, usually AI, work in that environment. Palantir created the role; OpenAI, Anthropic, Google and most AI companies now use FDEs because AI fails far more often in deployment than in the model.

It embeds with your team and first studies your existing systems, practices, rules and processes. From that it designs an AI plan that fits your business, then builds and deploys it, wires it into the tools your people already use, measures it against your baseline, and stays until it works and your team can run it.

Consultants analyse and recommend, then leave before anything is built. Staff augmentation adds engineers who build whatever they are told. An FDE squad does the discovery itself, writes the production code, integrates it with your systems and is accountable for the business result, then hands it over so your team can run it.

Individual AI specialists can build a model, a prompt or a demo. We are a software company with a full team behind every squad, so we also build everything the AI needs around it: web and mobile apps, UI/UX, admin panels, dashboards, integrations, testing, DevOps and support. One accountable team owns the whole software lifecycle, not just the AI part.

AI models are now capable enough that most failures are deployment failures: the wrong data access, missing integrations, no evals, no owner. MIT's 2025 NANDA study reported that around 95% of generative AI pilots delivered no measurable profit impact. Companies want engineers who close that gap inside the business, and experienced FDEs are scarce and expensive to hire directly.

When AI has to work inside messy, real operations: several systems that do not talk to each other, compliance rules, legacy software, teams with their own ways of working, or pilots that looked good in a demo but never reached production. If an off-the-shelf tool would do the job, you do not need an FDE squad.

It depends on the complexity of your systems, data access, approvals and the bigger picture of what you want AI to change. We spend dedicated time understanding your business first, then share a plan with realistic milestones before any build starts, instead of promising a date before we know your operations.

A squad sized to the problem: a deployment lead who owns the outcome, AI engineers for agents, retrieval and evals, integration engineers for your systems and data, and reliability engineering for guardrails, monitoring and cost. Behind it is our full team for UI/UX, web and mobile apps, QA and DevOps. Your domain experts are part of the squad from day one.

It is flexible and agreed after a scoping conversation: a monthly squad, a project quote or a fixed lump sum for a defined scope, whichever suits how you want to work.

We are remote-first from India with agreed daily overlap in your time zone, and we work with clients worldwide. On-site visits for discovery and go-live can be arranged where they help.

We are model-agnostic: Anthropic Claude, OpenAI, Google Gemini, Azure OpenAI, AWS Bedrock and open-weight models, on AWS, Azure or GCP. We choose against your own test cases, security rules and budget, and keep the model layer swappable.

You do. Everything the squad writes lives in your repositories and your cloud, under your accounts, from the first commit.

Your engineers have paired with ours throughout, and you get runbooks, documentation and the eval suite. You can keep a smaller squad on call for monitoring and model upgrades, extend to the next workflow, or run it entirely yourselves.

It is one AI knowledge layer over the systems your business already uses, such as email, CRM, documents, call notes and data. It keeps a single source of truth, respects permissions, answers with citations and learns from corrections, so your AI tools and your people work from the same facts.

A chatbot is one way of asking questions; a wiki has to be updated by hand and goes stale; search finds documents but leaves you to work out which one is right. A company brain decides which source is the truth, keeps it current, enforces who may see what, and gives one answer with its source, to people and to every AI agent you run.

Without it, every agent, chatbot or AI caller has its own partial copy of your knowledge, so they give different answers and forget what they learned. A shared brain means a correction made once applies everywhere, and every answer can be traced back to its source.

The brain follows the permissions of each source and each role. A sales rep, a support agent and an AI calling line each only get answers from content they are allowed to see. We set this up with your team before anything goes live.

No. We start with one high-value area, map which sources to trust, flag duplicates and outdated content, and improve the rest as we go. Finding the gaps is part of the work.

Off-the-shelf tools are good at search across popular apps. A built company brain makes sense when your knowledge sits in custom or legacy systems, when you need it inside your own cloud, or when your AI agents, chatbot and AI calling must share it. We will tell you honestly if an existing tool is enough.

It can run inside your own cloud account, using AI models under agreements that do not train on your data. You own the code and the content, with no lock-in.

It depends on how many sources and teams are involved and how complex your business is, so we agree scope after understanding it. Pricing stays flexible: a monthly engagement, a project quote or a lump sum.

When the task has genuinely different modes of work — planning, retrieval, generation, verification, side effects — or when you need to evaluate and improve parts independently. If a single well-prompted call with two tools does the job reliably, keep it.

Usually LangGraph for the agent graph and Temporal where runs are long-lived or must survive infrastructure failure. We keep the orchestration layer thin enough to swap, because this tooling moves quickly.

Classic RPA follows fixed scripts against fixed screens and breaks when either changes. We automate against APIs where they exist, and use models for the judgement steps — reading a document, classifying an intent, drafting a reply — with rules and verification around them.

Yes. Where a client is standardised on Microsoft we build with Power Platform, Azure OpenAI and Copilot extensibility; elsewhere we use Temporal or n8n with our own services. The judgement layer is portable either way.

Yes. That is the point of how we work. Every recommendation comes in plain language with the cost and the expected result, and a senior engineer walks you through it. You decide each step.

Usually not. Most of the benefit comes from connecting AI and automation to the systems you already run. We suggest replacing something only when it truly holds you back, and then one part at a time.

Yes. That is a very common starting point. We study the software and the data, watch how your staff use it, and document it before changing anything.

Both. Around two-thirds of this work is typically AI, and the rest is custom software, integrations and automation. We use whichever solves the problem best.

The review takes one to two weeks. The first automation or assistant is typically live a few weeks after that, measured against the numbers recorded at the start.

Constrain what the model can say: require retrieval with citations, verify claims against the retrieved span, and refuse when support is missing. Then measure it — an ungrounded-answer rate you track per release is what actually keeps it low.

Yes, and it is one of the most common ways we start. We instrument the existing system, build the dataset from its real traffic, and hand back a suite your team runs in CI.

Yes. We deploy inside your cloud account or VPC, use models with no-training data agreements or self-hosted open weights, and keep retrieval permission-aware so answers never cross a tenant or role boundary.

We have built retrieval over hundreds of thousands of pages. Scale is mostly an ingestion and indexing engineering problem; answer quality depends far more on chunking, metadata and reranking than on raw size.

Web, mobile app, WhatsApp Business, email and in-product surfaces, from one assistant definition so behaviour stays consistent across channels.

It says so and escalates. A confident wrong answer costs far more than a handover, so refusal behaviour is defined explicitly and tested in the eval suite.

Yes — we disclose it at the start of the call. Beyond being required in many jurisdictions, undisclosed AI callers damage trust the moment someone works it out.

Yes, with a warm transfer that passes the transcript and collected data to the agent who picks up, so the caller does not repeat themselves.

Model Context Protocol is an open standard for exposing tools and data to AI assistants over a consistent interface. Build the server once and any MCP-capable client can use it, instead of writing a bespoke integration per assistant.

Row-level with enforced policies covers most B2B SaaS and keeps operations simple. We move to schema or database per tenant when contracts demand physical separation, data residency differs per customer, or tenants are few and very large.

Yes. It is a migration project: introduce the tenancy boundary, backfill and verify, then cut over tenant by tenant with a rollback path at each step.

React Native for most products: one codebase, near-native feel, far cheaper to evolve. We go native when the app depends on heavy device capability — sustained camera processing, complex background work, deep platform integrations.

Yes. We start with an audit — architecture, test coverage, dependency and security risk — and give you a written assessment with options before we touch anything.

Yes — a till that stops when the internet does is not a till. We build local-first with a sync queue, conflict resolution and end-of-shift reconciliation.

Yes, and it usually works best that way — your admin holds the business context, we bring engineering capacity for the custom development and integrations.

Rates depend on seniority and skill, and we quote monthly per engineer with no placement fee. Tell us the role and the duration and you will get a number the same week.

Yes, and it often runs the other way too — teams start with a pod, then move a defined piece of work to a fixed-scope project once the requirements settle.