Techtiz designs, builds, and operates AI agents for production: retrieval over your documents, tool use and system integrations, and voice. Every agent ships with acceptance evidence, human approval gates on irreversible actions, a monthly cost ceiling, and rollback. One scoped workflow goes live in 6 to 8 weeks.
For workflows where a person reads the same screen, queries the same data, and clicks the same tools all day.
Gartner forecasts that more than 40% of agentic AI projects will be canceled by the end of 2027, and names 3 causes. Each is a decision made too late. We make all 3 first.
Cost gets a hard ceiling and per-run telemetry, before launch.
Value is defined in the specification as acceptance evidence, before code exists.
Risk gets human approval gates and rollback before the first customer sees it, not after the first incident.
Source: Gartner press release, 25 June 2025.
Read context, call your tools, and complete multi-step processes with exceptions handled and escalated. Proof layer: tool-use loops with typed schemas, idempotent actions, retry and backoff policy, state stored separately from the execution surface.
Over your own documents and data, with citations back to the source document on every answer. Proof layer: chunking and embedding strategy chosen per corpus, hybrid retrieval where lexical matters, reranking, answer grounding checks.
For intake, qualification, and follow-up, with full transcript logging. Proof layer: telephony, barge-in and latency budgets, structured extraction into your CRM, escalation on defined triggers.
Into your CRM, ticketing, ERP, and data stack through Model Context Protocol servers and native APIs. Proof layer: MCP where a governed tool surface is worth the overhead, native APIs where it is not. We do not add a protocol layer to justify a buzzword.
Not every workflow deserves a built agent on day 1, and pretending otherwise is how pilot budgets die. We prove the workflow in an assisted working session first. It gets promoted to a durable worker when 1 signal appears: the method has stopped changing. Promotion is 4 specific upgrades, not a rewrite: the working brief becomes a written specification, the eyeball check becomes an evaluation set, the person in the loop becomes a defined escalation rule, and the ad-hoc session becomes a governed runtime with logging and a cost ceiling. An agent built this way is mostly promotion, not invention. That is why it ships in weeks and holds in production.
Wide workflows get unbundled into narrow workers, each with its own specification, evaluation set, and owner. A worker asked to do too much cannot be specified precisely or evaluated cleanly.
This is not a cost decision. A single agent handling intake, qualification, scheduling, and billing has a failure surface nobody can reason about, and when it degrades you cannot tell which of the 4 jobs got worse.
Production requires knowing how often it succeeds, and on what.
50 to 200 graded cases per workflow at launch, drawn from your actual history including the ugly ones.
Every caught failure becomes a permanent regression case. The evaluation set only grows, which is what makes an agent improve month over month instead of oscillating.
Evaluations run before launch as a gate, on every change as a regression check, and on a schedule to catch drift when a model provider ships an update you did not ask for.
Written into the specification before the build starts. If the system does not clear it, it does not launch, a decision made in week 1 rather than argued about in week 8.
The engineering question is what happens next.
Escalates to a human with full context and the reasoning trace attached.
Says so and escalates. It does not answer from general knowledge.
Retries with backoff, then escalates. Actions are idempotent so retries cannot double-charge or double-send.
Blocked before it leaves the system, logged as a regression case.
Anything irreversible sits behind a human gate by default: sending, paying, deleting, publishing, and contacting a customer for the first time. Gates are removed later against evaluation data, never against optimism.
Every agent we build ships with these controls defined in the specification before code exists.
Every action is logged: what the agent read, which tool it called, what it produced, whether a human approved it, and when. Exportable without our help.
Which actions run autonomously and which require a human, named by role. Changing it is a written change, never a silent config flip.
What the agent may and may not read, and where each data class sits. A forwardable deliverable, not an internal note.
How long logs, traces, and embeddings are kept, and how a deletion request propagates through the retrieval index, not just the source database.
Model, prompt, tool schema, and evaluation set are all versioned, so any output traces back to the exact configuration that produced it.
NIST AI Risk Management Framework aware: govern, map, measure, and manage, sized for a mid-market company.
HIPAA-aware handling with a business associate agreement where we act as subcontractor. FERPA-aware handling for education data. Human approval on anything carrying a regulatory consequence in financial services.
What we are not: a regulatory advisory firm, and we do not certify compliance. We build systems that produce the evidence your compliance function needs, and we say so the moment your question needs counsel.
Most teams answer every failure by rewriting the prompt. That is why the same failure keeps returning: the fix was applied 1 to 3 layers above where the break happened.
Where most teams apply every fix, whether or not it is where the failure actually happened.
A retrieval gap is not a prompt problem.
A tool permission error is not a prompt problem.
A loop that never terminates is not a prompt problem.
Our runbooks name which layer actually broke and fix it there.
And you get told before it gets close. Per-run token and infrastructure telemetry against a hard ceiling set in the specification.
70% and 90% of monthly budget, with an automatic halt at 100%.
Estimated against expected monthly volume, with the volume assumption stated so it can be challenged.
Named in the estimate published before you sign.
Named in the estimate published before you sign.
Where voice is in scope, named in the estimate.
Named in the estimate published before you sign.
Model pricing moves. When it moves, your estimate is re-run and you are told, whether it moved up or down. Iteration 1: mechanism only. Currency figures restored in iteration 2 per decision D-07.
Retrieval runs over your data, in your environment or a dedicated tenant.
We use model provider APIs under enterprise terms that exclude your data from training. The provider and the terms are named in your specification.
Engineering is performed in Lahore, Pakistan, by Techtiz personnel under a US-formed Wyoming LLC. Access is scoped, logged, and revoked at exit.
Where a data class must not leave a jurisdiction or your boundary, we design around it and document what our side touches.
We do not hold SOC 2 Type II. If your security review requires it today, we are not your vendor yet and we say so on the first call rather than the fourth.
We monitor, tune, run scheduled evaluations, and respond within an agreed window.
Your team runs it. You get the runbook, the evaluation set, the dashboards, and 2 training sessions. We stay reachable for 30 days.
There is no third option where the agent quietly becomes unowned, because unowned agents are how the run cost becomes a surprise.
Agents that take irreversible action on customers without a human gate at launch.
Agents over data we are not permitted to see.
Agents whose success cannot be written as a checkable sentence. If we cannot evaluate it, we will not ship it.
A single agent asked to do 5 unrelated jobs.
Anything where an existing tool solves it. We will tell you the tool.
Your repositories, your IP, assigned on payment, from day 1.
30 days notice on anything monthly. Handover runbook included, not quoted separately.
4+ hours of daily overlap with your working day, contractual.
Work performed in Lahore by Techtiz personnel under a US-formed Wyoming LLC. Regulated data classes stay on your side and we design around that.
1 senior engineer accountable for the estimate, on the first call.
Every non-obvious statistic on this site carries a named third-party source and a date.
If your SLED scope calls for AI automation, RAG over a document corpus, or agent deployment, we build it behind the prime. The boundary is fixed on purpose.
NDA-first, subcontract-only. We work behind the prime. We do not pursue prime contracts and we never face the agency.
Capability over claims. Model-agnostic agent architectures, retrieval systems, and workflow automation, mapped to your bid’s technical scope.
Governance built in. The audit trail, approval matrix, and data boundary map are part of every agent we ship, the controls a procurement security review asks for.
Scoped tools, retrieval over approved sources only, automated verification on outputs, an evaluation gate before deploy, and a human gate on anything irreversible. When retrieval finds nothing, the agent says so instead of inventing an answer.
Whichever wins the evaluation on your cases. We are model-agnostic by contract and re-test as models change. A vendor that locks you to 1 model is optimising for their relationship, not your result.
You do, all 3, in your repository. The evaluation set is often the most valuable artifact of the engagement and it is yours.
No. Enterprise API terms that exclude training, named in your specification.
6 to 8 weeks for 1 scoped workflow. If your case history is missing or your integrations are undocumented, we say so in scoping and quote the discovery separately rather than absorbing it into an optimistic timeline.
Yes. Every action, tool call, output, and human approval is logged and exportable, and model, prompt, and evaluation-set versions are tracked so any output traces back to the configuration that produced it.
Written by Adnan Naeem, Chief Technology Officer
View LinkedIn →Tell us the workflow someone works by hand all day. We will tell you whether an agent fits, and what it takes to ship it safely.
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